System and method for automated hybrid network creation

ABSTRACT

A global architecture (GLP), as disclosed herein, is based on the thin server architectural pattern; it delivers all its services in the form of web services and there are no user interface components executed on the GLP. Each web service exposed by the GLP is stateless, which allows the GLP to be highly scalable. The GLP is further decomposed into components. Each component is a microservice, making the overall architecture fully decoupled. Each microservice has fail-over nodes and can scale up on demand. This means the GLP has no single point of failure, making the platform both highly scalable and available. The GLP architecture provides the capability to build and deploy a microservice instance for each course-recipient-user combination. Because each student interacts with their own microservice, this makes the GLP scale up to the limit of cloud resources available—i.e. near infinity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/656,912 filed Apr. 12, 2018 and entitled SYSTEMS AND METHODS FORMICRO SERVICE-BASED CONTENT PROVISIONING AND DELIVERY; the entirety ofwhich is incorporated herein by reference.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, i.e., the communications protocols to organize network traffic,the network's size, topology and organizational intent. In most cases,communications protocols are layered on other more specific or moregeneral communications protocols, except for the physical layer thatdirectly deals with the transmission media.

BRIEF SUMMARY

A global learning platform (GLP), also referred to herein as a globalarchitecture, as disclosed herein, can be based on the thin serverarchitectural pattern; and can deliver some or all its services in theform of web services; and there can be no user interface componentsexecuted on the GLP. Each web service exposed by the GLP can bestateless, which can allow the GLP to be highly scalable. The GLP isfurther decomposed into components. Each component is a microservice,making the overall architecture fully decoupled. Each microservice hasfail-over nodes and can scale up on demand. This means the GLP has nosingle point of failure, making the platform both highly scalable andavailable. The GLP architecture provides the capability to build anddeploy a microservice instance for each course-recipient-usercombination. Because each student interacts with their own microservice,this makes the GLP scale up to the limit of cloud resourcesavailable—i.e. near infinity.

One aspect of the present disclosure relates to a system for hybridcontent provisioning of remote content stored on a remote contentplatform and native content stored within the system. The systemincludes: a memory including: a content library database containingnative content; and an asset database containing a plurality ofpackage-data assets each including a link directing to content. In someembodiments, the content includes native content and remote content,which remote content can be stored on the remote content platform. Thesystem can include at least one server. The at least one server caninclude: a communications microservice; a data packaging module; a modelbuilding module; and an engagement delivery module. In some embodiments,each of the data packaging module, the model building module, and theengagement delivery module can send data to the communicationsmicroservice and receive a digest from the communications microservice.In some embodiments, the at least one server can: receive first contentinformation associated with first content stored on the remote contentplatform; package the received first content information into a firstpackage-data asset including a link directing to first content stored onthe remote content platform and enrich the first package-data asset withmetadata relating to the received first content, which received firstcontent information is packaged and which first package-data asset isenriched via the data packaging module; receive a first content request;identify a first package-data asset associated with remote content;deliver the first package-data asset to a user device; receive a secondcontent request; identify a second package-data asset associated withnative content; and deliver the second package-data asset to the userdevice.

In some embodiments, the first package-data asset is deliverable to theuser device subsequent to receipt of an indicator of completion ofenriching from the digest from the communications microservice. In someembodiments, the at least one server can: receive second contentinformation associated with the second content, which second contentincludes native content; package the received second content informationinto a second package-data asset comprising a link directing to thesecond content; and enrich the second package-data asset with metadatarelating to the second content. In some embodiments, the received secondcontent information is packaged via the data packaging module, andsecond package-data asset is enriched via the data packaging module.

In some embodiments, the at least one server can: store the firstpackage-data asset and the second package-data asset in the assetdatabase. In some embodiments, the metadata enriching the firstpackage-data asset identifies a first content type and the metadataenriching the second package-data asset identifies a second contenttype. In some embodiments, the first content type specifies a file typeof the first content linked by the first package-data asset and thesecond content type specifies a file type of the second content linkedby the second package-data asset. In some embodiments, the file type ofthe first content is the same as the file type of the second content. Insome embodiments, the at least one server can determine a type of eachof the first package-data asset and the second package-data asset.

One aspect of the present disclosure relates to a method for hybridcontent provisioning of remote content stored on a remote contentplatform and native content. The method includes: receiving at least oneserver first content information associated with first content stored ona remote content platform; packaging with the at least one server thereceived first content information into a first packaged data-assetincluding a link directing to content stored on the remote contentplatform; enriching with the at least one server the first packageddata-asset with metadata relating to the received content; receiving atthe server a first content request from a first user device; identifyinga first package-data asset associated with remote content; deliveringthe first package-data asset to the first user device; receiving at theserver a second content request from the first user device; identifyinga second package-data asset associated with native content; anddelivering the second package-data asset to the first user device.

In some embodiments, the at least one server includes: a communicationsmicroservice; a data packaging module; a model building module; and anengagement delivery module. In some embodiments, the received firstcontent information is packaged via the data packaging module, and thefirst package data-asset is enriched via the data packaging module. Insome embodiments, each of the data packaging module, the model buildingmodule, and the engagement delivery module send data to thecommunications microservice and receive a digest from the communicationsmicroservice.

In some embodiments, the first package-data asset is deliverable to theuser device subsequent to receipt of an indicator of completion ofenriching from the digest from the communications microservice. In someembodiments, the method includes: receiving second content informationassociated with the second content, which second content includes nativecontent; packaging the received second content information into a secondpackage-data asset including a link directing to the second content; andenriching the second package-data asset with metadata relating to thesecond content. In some embodiments, the received second contentinformation is packaged via the data packaging module, and the secondpackage-data asset is enriched via the data packaging module. In someembodiments, the method includes storing the first package-data assetand the second package-data asset in an asset database containing aplurality of package-data assets each enriched with metadata and eachincluding a link directing to associated content.

In some embodiments, the metadata enriching the first package-data assetidentifies a first content type and the metadata enriching the secondpackage-data asset identifies a second content type. In someembodiments, the first content type specifies a file type of the firstcontent linked by the first package-data asset and the second contenttype specifies a file type of the second content linked by the secondpackage-data asset. In some embodiments, the file type of the firstcontent is the same as the file type of the second content. In someembodiments, the method includes: determining a type of each of thefirst package-data asset and the second package-data asset.

One aspect of the present disclosure relates to a system for stackedmicroservice based content provisioning. The system includes a memoryincluding: a content library database containing native content; and anasset database including a plurality of package-data assets eachcontaining a link directing to content. In some embodiments, the contentincludes native content and remote content, which remote content isstored on the remote content platform. The system can include at leastone processing resource including: a communications microservice; a datapackaging module; a model building module; and an external communicationmodule. In some embodiments, each of the data packaging module and themodel building module can send data to the communications microserviceand receive a digest from the communications microservice. In someembodiments, each of the modules includes a fail-over node. In someembodiments, the at least one processing resource can: receive a contentrequest from a user device via the external communications module at anengagement delivery module; determine with the engagement deliverymodule to request information from at least one other module of the atleast one processing resource; receive data at the communicationsmicroservice from the engagement delivery module, which received data isindicative of requested information; generate and output a digest withthe communications microservice including the received data; receive thedigest with at least one of the data packaging module; and the modelbuilding module; receive requested information at the engagementdelivery module; and deliver requested information to a user device fordelivery to a user.

In some embodiments, the at least one processing resource can: identifythe requested information with the at least one of the data packagingmodule; and the model building module; and deliver the requestedinformation to the engagement delivery module. In some embodiments, theat least one of the data packaging module; and the model building modulecan: identify relevant information from within the received digest; andidentify the requested information based on the relevant informationfrom within the received digest.

In some embodiments, the at least one processing resource can be aplurality of servers. In some embodiments, the plurality of servers canbe arranged to provide each of the modules of the at least oneprocessing resource with at least one fail-over node. In someembodiments, the at least one processing resource is configured suchthat each of the modules of the at least one processing resource isindependently scalable.

In some embodiments, the communications module includes: an interprocess communication service microservice; and notification servicesmicroservice. In some embodiments, the inter process communicationservice microservice implements a message passing interface. In someembodiments, the delivered requested information includes at least onepackage-data asset. In some embodiments, the package-data asset includesmetadata and a link to content associated with the package-data asset.In some embodiments, the content associated with the package-data assetis stored remote from the memory and the at least one processingresource. In some embodiments, the engagement delivery module is locatedin the at least one processing resource. In some embodiments, theengagement delivery module is located on the user device.

One aspect of the present disclosure relates to method for stackedmicroservice based content provisioning. The method includes: receivinga content request from a user device via an external communicationsmodule at an engagement delivery module of at least one processingresource; determining with the engagement delivery module to requestinformation from at least one other module of the at least oneprocessing resource; receiving data at a communications microservicefrom the engagement delivery module, which received data is indicativeof requested information; generating and outputting a digest with thecommunications microservice, the digest including the received data;receiving the digest with at least one of a data packaging module; and amodel building module; receiving requested information at the engagementdelivery module; and delivering requested information to a user devicefor delivery to a user.

In some embodiments, the method includes: requesting information from atleast one other module of the at least one processing resource;identifying the requested information with the at least one of the datapackaging module; and the model building module; and delivering therequested information to the engagement delivery module. In someembodiments, the at least one of the data packaging module; and themodel building module can: identify relevant information from within thereceived digest; and identify the requested information based on therelevant information from within the received digest.

In some embodiments, the at least one processing resource includes aplurality of servers. In some embodiments, the plurality of servers arearranged to provide each of the modules of the at least one processingresource with at least one fail-over node. In some embodiments, each ofthe modules of the at least one processing resource is independentlyscalable.

In some embodiments, the delivered requested information includes atleast one package-data asset. In some embodiments, the package-dataasset includes metadata and a link to content associated with thepackage-data asset. In some embodiments, the content associated with thepackage-data asset is stored remote from the memory and the at least oneprocessing resource. In some embodiments, the engagement delivery moduleis located on the user device.

One aspect of the present disclosure relates to a system for redundantcontent communications. The system includes a memory including an assetdatabase containing a plurality of package-data assets each containing alink directing to content. In some embodiments, the content can nativecontent and/or can be remote content. In some embodiments, the remotecontent is stored on a remote content platform. The system can includeat least one server. The at least one server can include: acommunications microservice; a data packaging module; a model buildingmodule; and an engagement delivery module. In some embodiments, each ofthe data packaging module, the model building module, and the engagementdelivery module can send data to the communications microservice andreceive a digest from the communications microservice. In someembodiments, the at least one server can: receive a connection requestat the least one server from a user device; allow connection with theuser device; establish a first connection with the user device via afirst API, which first API receives data from the user device andprovides data to the user device; establish a second connection with theuser device via a second API, which second API receives data from theuser device, and which data received by the second API from the userdevice is at least partially redundant to data received by the first APIfrom the user device.

In some embodiments, the connection request comprises a unique useridentifier. In some embodiments, the data received from the user deviceby the first API includes response and request information. In someembodiments, the data received from the user device by the second APIincludes user activity information. In some embodiments, the useractivity information includes response information and requestinformation.

In some embodiments, the at least one server can: ingest stream ofactivity information with the second API; and generate events from thestream of activity information with the second API. In some embodiments,the at least one server can push the generated events to recipientmicroservices. In some embodiments, the second connection is establishedvia receipt of a token from the user device and validation of the token.In some embodiments, the at least one server can: receive a user inputvia the first API, wherein the user input identifies a state of theuser; and provide a response to the user device via the first API basedin part on the state identified in the user input. In some embodiments,the first API is stateless.

One aspect of the present disclosure relates to a method of redundantcontent communications. The method includes: receiving a connectionrequest at at least one server from a user device; allowing connectionwith the user device; establishing a first connection with the userdevice via a first API, which first API receives data from the userdevice and provides data to the user device; establishing a secondconnection with the user device via a second API, which second APIreceives data from the user device, and which data received by thesecond API from the user device is at least partially redundant to datareceived by the first API from the user device.

In some embodiments, the connection request can be a unique useridentifier. In some embodiments, the data received from the user deviceby the first API includes response and request information. In someembodiments, the data received from the user device by the second APIincludes user activity information. In some embodiments, the useractivity information includes response information and requestinformation.

In some embodiments, the method includes: ingesting stream of activityinformation with the second API; and generating events from the streamof activity information with the second API. In some embodiments, themethod includes pushing the generated events to recipient microservices.In some embodiments, the second connection is established via receipt ofa token from the user device and validation of the token. In someembodiments, the method includes: receiving a user input via the firstAPI, which user input identifies a state of the user; and providing aresponse to the user device via the first API based in part on the stateidentified in the user input. In some embodiments, the first API isstateless.

One aspect of the present disclosure relates to a system for automaticgeneration of a package-data asset. The system includes a memoryincluding an asset database containing a plurality of package-dataassets each including a link directing to content. In some embodiments,the content can be remote content. In some embodiments, the remotecontent is stored on a remote content platform. The system can includeat least one server communicatingly coupled with the memory. The atleast one server including a data packaging microservice. In someembodiments, the data packaging microservice can: receive at least oneserver first content information associated with first content; parsethe received first content information; extract semantic elements fromthe received first content information, which semantic elements areembedded in the received first content information; package the receivedfirst content information into a first package-data asset including alink directing to the first content; and enrich the first package-dataasset with metadata relating to the first content.

In some embodiments, the data packaging microservice includes: asemantic enricher; an asset manager; and a resource manager. In someembodiments, the semantic enricher can extract the semantic elementsfrom the received first content information and enrich the firstpackage-data asset. In some embodiments, the first content informationincludes metadata associated with a piece of content. In someembodiments, the first content information identifies a location ofcontent associated with the first content information. In someembodiments, information identifying a location can includes at leastone of a directory path; and a uniform resource locator.

In some embodiments, the received content information is parsed by anatural language processing algorithm. In some embodiments, the at leastone server can store the first package data asset in the memory. In someembodiments, the data packaging microservice is communicatingly coupledwith a recipient-user microservice that can deliver content associatedwith the package data asset to a user. In some embodiments, therecipient-user microservice is customized to the user.

One aspect of the present disclosure relates to a method of automaticgeneration of a package-data asset. The method includes: receiving at adata packaging microservice of at least one server first contentinformation associated with first content; parsing the received firstcontent information; extracting semantic elements from the receivedfirst content information, which semantic elements are embedded in thereceived first content information; packaging the received first contentinformation into a first package-data asset containing a link directingto the first content; and enriching the first package-data asset withmetadata relating to the first content.

In some embodiments, the data packaging microservice includes: asemantic enricher; an asset manager; and a resource manager. In someembodiments, the semantic enricher can extract the semantic elementsfrom the received first content information and enrich the firstpackage-data asset. In some embodiments, the first content informationincludes metadata associated with a piece of content. In someembodiments, the first content information identifies a location ofcontent associated with the first content information.

In some embodiments, information identifying a location can include atleast one of a directory path; and a uniform resource locator. In someembodiments, the received content information is parsed by a naturallanguage processing algorithm. In some embodiments, the method includesstoring the first package data asset in a memory. In some embodiments,the data packaging microservice is communicatingly coupled with arecipient-user microservice configured to delivery content associatedwith the package data asset to a user. In some embodiments, the methodincludes generating the recipient-user microservice for the user.

One embodiment of the present disclosure relates to a system for hybridcontent graph creation. The system includes a memory including: an assetdatabase containing a plurality of package-data assets each including alink directing to content, at least some of the content is stored on aremote content platform; and a capability database including informationidentifying capabilities of a plurality of microservices. The system caninclude at least one server including the plurality of microservices.The at least one server can: identify a set of package-data assets fromthe asset database of the memory; retrieve a content model associatedwith the set of package-data assets; automatically determine asequencing of package-data assets in the set of package-data assets,which sequencing is partially adaptive-based and partiallynon-adaptive-based; and generate a graphical representation of the setof package-data assets based on the determined sequencing.

In some embodiments, the content model is retrieved from the memory. Insome embodiments, the at least one server can identify a first groupincluding at least some of the set of package-data assets as having afirst asset type and identify a second group including at least some ofthe set of package-data assets as having a second asset type. In someembodiments, the at least some of the set of package-data assetsincluded in the first group are different than the at least some of theset of package-data assets included in the second group. In someembodiments, the first asset type includes content-type package-dataassets and the second asset type includes non-content-type package-dataassets.

In some embodiments, the at least one server can: (a) select a one ofthe package-data assets in the set of package-data assets; (b) determinea sequencing type for the selected one of the package-data assets, whichsequencing type identifies at least one of adaptive sequencing,non-adaptive sequencing, and partially adaptive sequencing; and repeat(a) and (b) until a sequencing type for all of package-data assets inthe set of package data assets is determined. In some embodiments,determining the sequencing includes, for non-adaptive package-dataassets: retrieving sequencing information; and sequencing thenon-adaptive package-data assets according to the retrieved sequencinginformation. In some embodiments, determining the sequencing includes,for the adaptive package-data assets: determining a skill level forpackage-data assets having and adaptive sequencing type; and identifyingprerequisite relationships between the package-data assets. In someembodiments, the at least one server can store non-content typepackage-data assets in the memory. In some embodiments, the at least oneserver can link content-type package-data assets and non-content-typepackage-data assets.

One aspect of the present disclosure relates to a method of automatichybrid content graph creation. The method includes: identifying a set ofpackage-data assets; retrieving a content model associated with the setof package-data assets; automatically determining an initial sequencingof package-data assets in the set of package-data assets, whichsequencing is partially adaptive-based and partially non-adaptive-based;and generating a graphical representation of the set of package-dataassets based on the determined sequencing.

In some embodiments, the content model is retrieved from a memory. Insome embodiments, the method includes identifying a first groupincluding at least some of the set of package-data assets as having afirst asset type and identifying a second group including at least someof the set of package-data assets as having a second asset type. In someembodiments, the at least some of the set of package-data assetsincluded in the first group are different than the at least some of theset of package-data assets included in the second group. In someembodiments, the first asset type includes content-type package-dataassets and the second asset type includes non-content-type package-dataassets.

In some embodiments, the method includes: (a) selecting a one of thepackage-data assets in the set of package-data assets; (b) determining asequencing type for the selected one of the package-data assets. In someembodiments, the sequencing type identifies at least one of adaptivesequencing, non-adaptive sequencing, and partially adaptive sequencing.In some embodiments, the method includes repeating (a) and (b) until asequencing type for all of package-data assets in the set of packagedata assets is determined. In some embodiments, determining thesequencing includes, for non-adaptive package-data assets: retrievingsequencing information; and sequencing the non-adaptive package-dataassets according to the retrieved sequencing information. In someembodiments, determining the sequencing includes, for the adaptivepackage-data assets: determining a skill level for package-data assetshaving and adaptive sequencing type; and identifying prerequisiterelationships between the package-data assets. In some embodiments, themethod includes storing non-content type package-data assets in thememory. In some embodiments, the method includes linking content-typepackage-data assets and non-content-type package-data assets.

One aspect of the present disclosure relates to a system for automatedpersonalized microservice generation. The system includes a memoryincluding: a custom microservice database including data identifyinggenerated customized microservices and associated users; and an assetdatabase including a plurality of package-data assets. The system caninclude and at least one server including an engagement deliverymicroservice. In some embodiments, the engagement delivery microserviceincludes a microservices builder module. In some embodiments, the atleast one server can: receive a content request identifying requestedcontent from a user device; determine prior creation of a custom usermicroservice corresponding to the content request; retrieve package-dataasset information relevant to the content request; create a newmicroservice when a custom user microservice was not previously created,which new microservice corresponds to the received content request; anddeliver the requested content to the user device via the newmicroservice.

In some embodiments, the content request identifies a user, requestedcontent, and a content group. In some embodiments, the at least oneserver can identify capability requirements associated with the contentrequest. In some embodiments, the capability requirements correspond toa content type of the requested content. In some embodiments, deliveringthe requested content to the user device includes rendering therequested content with the new microservice.

In some embodiments, the at least one server can direct downloading ofthe new microservice to the user device. In some embodiments, thedownloading of the new microservice to the user device includes creationof a copy of the new microservice on the user device. In someembodiments, the new microservice includes an independent recommendationengine. In some embodiments, the independent recommendation engineincludes a machine-learning algorithm trained to identify next contentbased on an attribute of the next content and of the user. In someembodiments, the new microservice is specific to the user and to thecontent group.

One aspect of the present disclosure relates to a method of automatedpersonalized microservice generation. The method includes: receiving atleast one server a content request identifying requested content from auser device; determining prior creation of a custom user microservicecorresponding to the content request; retrieving package-data assetinformation relevant to the content request; creating with the at leastone server a new microservice when a custom user microservice was notpreviously created, which new microservice corresponds to the receivedcontent request; and delivering the requested content to the user devicevia the new microservice.

In some embodiments, the content request identifies a user, requestedcontent, and a content group. In some embodiments, the method includesidentifying capability requirements associated with the content request.In some embodiments, the capability requirements correspond to a contenttype of the requested content. In some embodiments, delivering therequested content to the user device includes rendering the requestedcontent with the new microservice.

In some embodiments, the method includes directing downloading of thenew microservice to the user device. In some embodiments, thedownloading of the new microservice to the user device includes creationof a copy of the new microservice on the user device. In someembodiments, the new microservice includes an independent recommendationengine. In some embodiments, the independent recommendation engineincludes a machine-learning algorithm trained to identify next contentbased on an attribute of the next content and of the user. In someembodiments, the new microservice is specific to the user and to thecontent group.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of the user deviceand supervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of the globalarchitecture in a personal content delivery system.

FIG. 9 is a schematic illustration of combinations of products andcontent to create product models.

FIG. 10 is an overview block diagram of the components of the globalarchitecture.

FIG. 11 is a graphical depiction of scalability.

FIG. 12 is a schematic illustration of one embodiment of the APImanagement and security microservice.

FIG. 13 is a flowchart illustrating one embodiment of a process forhybrid content provisioning.

FIG. 14 is a flowchart illustrating one embodiment of a process forhybrid content provisioning.

FIG. 15 is a flowchart illustrating one embodiment of a process forredundant content communication.

FIG. 16 is a schematic illustration of the core data servicesmicroservice.

FIG. 17 is a schematic illustration of one embodiment of the datapackaging module.

FIG. 18 is a flowchart illustrating one embodiment of a process forautomatic generation of a package-data asset.

FIG. 19 is a schematic illustration of one embodiment of the learningmodel builder microservice.

FIG. 20 is a schematic illustration of one embodiment of processes forupdating models.

FIG. 21 is a schematic illustration of the learning product buildermicroservice.

FIG. 22 is a schematic illustration of the learning experience composermicroservice.

FIG. 23 is a flowchart illustrating one embodiment of a process forautomatic generation of a hybrid content graph.

FIG. 24 is a schematic illustration of one embodiment of usermicroservice.

FIG. 25 is a schematic illustration of one embodiment of the usermicroservice.

FIG. 26 a flowchart illustrating one embodiment of a process forautomated personalized microservice generation is shown

FIG. 27 is a flowchart illustrating one embodiment of a process forcontent delivery via an individualized and secured content deliverymicroservice.

FIG. 28 is a flowchart illustrating one embodiment of a process foroff-line operation of the user microservice.

FIG. 29 is a schematic illustration of one embodiment of the analyticscomponents.

FIG. 30 is a flowchart illustrating one embodiment of a process forhybrid event processing.

FIG. 31 is a schematic illustration of the cross cutting servicesmicroservice.

FIG. 32 depicts a detailed block diagram of the enabling servicescomponent in connection to the service orchestrator component.

FIG. 33 is a detailed block diagram of one or several applications.

FIG. 34 is detailed block diagram of one embodiment of a clientarchitecture of a client application.

FIG. 35 is a depiction of one embodiment of a content data asset.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing unit, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination of computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provide access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming systems, business or homeappliances, and/or personal messaging devices, capable of communicatingover network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such as awireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 114, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including his or her user device 106, contentresources accessed, and interactions with other user devices 106. Thisdata may be stored and processed by the user data server 114, to supportuser tracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include one or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser-based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof the same entities as server 202. For example, components 208 mayinclude one or more dedicated web servers and network hardware in adatacenter or a cloud infrastructure. In other examples, the securityand integration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the O Auth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XML,encryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such aspublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-311 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-311, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-311 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user mayhave one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, thestudent's learning style can be any learning style describing how thestudent best learns or how the student prefers to learn. In oneembodiment, these learning styles can include, for example,identification of the student as an auditory learner, as a visuallearner, and/or as a tactile learner. In some embodiments, the dataidentifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

The user profile database 301 can include a microservice sub-database.The microservice sub-database can include information identifying one orseveral custom user microservices, also referred to herein as learnermicroservices. In some embodiments, these learner microservices can forma part of the learning engagement delivery microservice 1020 to bediscussed at greater length below. In some embodiments, for example, acustom user microservice can be generated for each user, such that eachuser has a unique custom user microservice. In some embodiments, acustom user microservice can be generated for each unique combination ofcourse and user such that if a first user was enrolled in a first courseand a second course, a first custom user microservice would be generatedfor the first user in the first course and a second custom usermicroservice would be generated for the first user in the second course.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, these datapackets in the content library database 303 can be linked to form anobject network. In some embodiments, these data packets can be linked inthe object network according to one or several prerequisiterelationships that can, for example, identify the relative hierarchyand/or difficulty of the data objects. In some embodiments, thishierarchy of data objects can be generated by the content distributionnetwork 100 according to user experience with the object network, and insome embodiments, this hierarchy of data objects can be generated basedon one or several existing and/or external hierarchies such as, forexample, a syllabus, a table of contents, or the like. In someembodiments, for example, the object network can correspond to asyllabus such that content for the syllabus is embodied in the objectnetwork.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network, also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user;relation to a common subject, topic, skill, or the like; creation from acommon set of source material such as source data packets; or the like.In some embodiments, the content aggregation can comprise a grouping ofcontent comprising the presentation portion that can be provided to theuser in the form of, for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets that can be, for example, organized in object network. In someembodiments, the one or several content aggregations can be each createdfrom content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and, in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network can be organized according to one or several tasksidentified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309,can store information relating to one or several predictive models. Insome embodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Performance Factor Analysis (PFA), or the like. Theevidence model can, in some embodiments, be customizable to a userand/or to one or several content items. Specifically, one or severalinputs relating to the user and/or to one or several content items canbe inserted into the evidence model. These inputs can include, forexample, one or several measures of user skill level, one or severalmeasures of content item difficulty and/or skill level, or the like. Thecustomized evidence model can then be used to predict the likelihood ofthe user providing desired or undesired responses to one or several ofthe content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a user such as a student-user failing to achieve a desiredoutcome such as, for example, failing to correctly respond to one orseveral data packets, failure to achieve a desired level of completionof a program, for example in a pre-defined time period, failure toachieve a desired learning outcome, or the like. In some embodiments,the risk probability can identify the risk of the student-user failingto complete 60% of the program.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of the program such as, for example, a completion stage and/orcompletion status of the program. In one embodiment, for example, thefirst model function can be associated with a first completion status,the second model function can be associated with a second completionstatus, the third model function can be associated with a thirdcompletion status, and the fourth model function can be associated witha fourth completion status. In some embodiments, these completionstatuses can be selected such that some or all of these completionstatuses are less than the desired level of completion of the program.Specifically, in some embodiments, these completion statuses can beselected to all be at less than 60% completion of the program, and morespecifically, in some embodiments, the first completion status can be at20% completion of the program, the second completion status can be at30% completion of the program, the third completion status can be at 40%completion of the program, and the fourth completion status can be at50% completion of the program. Similarly, any desired number of modelfunctions can be associated with any desired number of completionstatuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a student-user's progress through a program.In some embodiments, the student-user's progress can be compared to oneor several status trigger thresholds, each of which status triggerthresholds can be associated with one or more of the model functions. Ifone of the status triggers is triggered by the student-user's progress,the corresponding one or several model functions can be selected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program status parameters. In some embodiments, each of the modelfunctions can use the same program status parameters, and in someembodiments, the model functions can use different program statusparameters. In some embodiments, the model functions use differentprogram status parameters when at least one of the model functions usesat least one program status parameter that is not used by others of themodel functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several students. In someembodiments, this model can identify a single skill level of a studentand/or a range of possible skill levels of a student. In someembodiments, this statistical model can identify a skill level of astudent-user and an error value or error range associated with thatskill level. In some embodiments, the error value can be associated witha confidence interval determined based on a confidence level. Thus, insome embodiments, as the number of student interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

A threshold source 310, also referred to herein as a threshold database,can store one or several threshold values. These one or severalthreshold values can delineate between states or conditions. In oneexemplary embodiment, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education-related data, consumer sales data,health-related data, and the like. Illustrative external dataaggregators 311 may include, for example, social networking web servers,public records data stores, learning management systems, educationalinstitution servers, business servers, consumer sales data stores,medical record data stores, etc. Data retrieved from various externaldata aggregators 311 may be used to verify and update user accountinformation, suggest user content, and perform user and contentevaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering This embodiment allows reporting timely information that mightbe of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: contentprovided to users; content consumed by users; responses provided byusers; user skill levels; content difficulty levels; next content forproviding to users; etc. The internal components 402-408 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The content management server102 can opt to allow third parties to use internally or externallygathered information that is aggregated within the server 102 bysubscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 238 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time or along another time line. There is a definedAPI between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith response reported that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like SCTP andUDP could be used in some embodiments to intake the gatheredinformation. A protocol such as SSL could be used to protect theinformation over the TCP connection. Authentication and authorizationcan be performed to any user devices 106 and/or supervisor deviceinterfacing to the server 102. The security and/or integration hardware410 receives the information from one or several of the user devices 106and/or the supervisor devices 110 by providing the API and anyencryption, authorization, and/or authentication. In some cases, thesecurity and/or integration hardware 410 reformats or rearranges thisreceived information.

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. Specifically, in some embodiments,the messaging bus 412 can receive and output information from at leastone of the packet selection system, the presentation system, theresponse system, and the summary model system. In some embodiments, thisinformation can be output according to a “push” model, and in someembodiments, this information can be output according to a “pull” model.

As indicated in FIG. 4, processing subscribers are indicated by aconnector to the messaging bus 412, the connector having an arrow headpointing away from the messaging bus 412. Only data streams within themessaging queue 412 that a particular processing subscriber hassubscribed to may be read by that processing subscriber if received atall. Gathered information sent to the messaging queue 412 is processedand returned in a data stream in a fraction of a second by the messagingqueue 412. Various multicasting and routing techniques can be used todistribute a data stream from the messaging queue 412 that a number ofprocessing subscribers have requested. Protocols such as Multicast ormultiple Unicast could be used to distribute streams within themessaging queue 412. Additionally, transport layer protocols like TCP,SCTP and UDP could be used in various embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of message in a particularcategory. For example, a data stream can comprise all of the datareported to the messaging bus 412 by a designated set of components. Oneor more processing subscribers could subscribe and receive the datastream to process the information and make a decision and/or feed theoutput from the processing as gathered information fed back into themessaging queue 412. Through the CC interface 338 a developer can searchthe available data streams or specify a new data stream and its API. Thenew data stream might be determined by processing a number of existingdata streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users, and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regard to the components 402-408, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including for example, the user profile database301, the content library database 303, the model database 309, or thelike. In some embodiments, these one or several data packets can beadaptively selected and/or selected according to one or severalselection rules. In one embodiment, for example, the recommendationengine can retrieve information from the user profile database 301identifying, for example, a skill level of the user. The recommendationengine can further retrieve information from the content librarydatabase 303 identifying, for example, potential data packets forproviding to the user and the difficulty of those data packets and/orthe skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a student-userbased on one or several received responses from that student-user. Insome embodiments, one or several data packets can be eliminated from thepool of potential data packets if the prediction indicates either toohigh a likelihood of a desired response or too low a likelihood of adesired response. In some embodiments, the recommendation engine canthen apply one or several selection criteria to the remaining potentialdata packets to select a data packet for providing to the user. Theseone or several selection criteria can be based on, for example, criteriarelating to a desired estimated time for receipt of response to the datapacket, one or several content parameters, one or several assignmentparameters, or the like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the summary model system 404 may query one or more databases and/or datastore servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include a response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices 106,supervisor devices 110, and administrator servers 116, etc. Forinstance, response system 406 may receive, aggregate, and analyze userevaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several responses into one or severalobservables can include determining whether the one or several responsesare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several responsesinto one or several observables can include characterizing the degree towhich one or several responses are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features, contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2, and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrative servers 116, and other devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater).

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to the user in perceptible and/orinterpretable form, and may receive one or several inputs from the userby generating one or several electrical signals based on one or severaluser-caused interactions with the I/O subsystem such as the depressingof a key or button, the moving of a mouse, the interaction with atouchscreen or trackpad, the interaction of a sound wave with amicrophone, or the like.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 518 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that, when executed by aprocessor, provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 500 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storingtransmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 532 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 311). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7, the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to a page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using another device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

FIG. 8 is a block diagram of the global architecture 810 in a personalcontent delivery system 800 shown in the context of the components justdescribed. The global architecture 810 is shown in an overview blockdiagram in FIG. 10, more fully described below.

The global architecture 810 can facilitate in one or several users, alsoreferred to herein as learners or recipient-users, achieving one orseveral learning objectives, also referred to herein as one or severalobjectives. In some embodiments, an objective can be a statement or textstring describing a goal for a recipient-user who is seeking mastery orknowledge of a certain domain or skill or topic. The attainment of theseobjectives can be facilitated by resources, which resources can be ameans of achieving and/or evaluating/assessing the objective. Resourcescan be of different categories. A resource of category type “content”can be content provisioned into the global architecture 810. A resourceassociates metadata to content such as resource title, resource type,delivery mode, etc. A resource category type “inline-text” indicatesthat text content is embedded inside the resource. A resource categorytype “widget” can be UI widget deployed in a computing environment suchas on the server 102. A resource can also be of non-content type such as“Ask a Question” which can be included as a part of a chatting softwaretool.

In some embodiments, the global architecture 810 contains content, insome embodiments, the global architecture 810 contains only metadataabout content and/or connections to content, and in some embodiments theglobal architecture 810 contains both content and metadata aboutcontent. In some embodiments, the global architecture 810 does notcontain any content, but it contains metadata about content. The contentcan be an artifact, such as a digital artifact, available on a ContentManagement System (CMS) or on a web or a file on an intranet andaccessed through a URL.

In one embodiment, a portion of the global architecture 810 can receivecontent and/or information relating to that content and can extractand/or generate metadata from that content. In some embodiments, thismetadata can include a link which can be data identifying a locationand/or pointing to a location in which the content is stored. This linkcan include, for example, a URL, a pointer, a directory path, or thelike. In some embodiments, the global architecture 810 can take thislink and enrich it with metadata. This combination of a link andmetadata is a resource. One or several resources can be packaged as alearning asset, also referred to herein as a package-data asset, whichlearning asset can be stored in the database server 104 or other memoryassociated with the global architecture 810. An asset can be any numberof things that can be content such as a video or an assessment item. Orit can be some other type of asset like notifications. The content canhave a known type, which type (narrative content, video, multiple choicequestion, etc.) can dictate the rendering. In some embodiments, thecontent and the type or types can be provisioned onto the globalarchitecture 810.

One embodiment of a content data asset is shown in FIG. 35. The contentdata asset 3500 includes a plurality of resources 3502. These resourceseach comprise metadata and a link 3505 coupling the resource to contentthat can be stored in a content repository such as, for example, thecontent platform 2820 of the enabling services 1070.

A product, as used herein can be an independently saleable and specificlearning experience. The product can include a set of metadatadescriptions of learning assets that can be used to identify content,formatting and display, as well as to enable platform features such associal (chat, collaboration, discussions, etc.), school andinstructional management, adaptive learning, and other digital features.A product model, as used herein, can be a grouping of products that havesimilar characteristics. This grouping is defined for business ratherthan technical reasons; i.e. all the products in a product model addressa similar business need. A product model organizes the content andnon-content learning assets to make available to particular students ina particular sequence. A product platform is a set of technologies thatsupports one or more product models, and/or describes how to sequencethe content.

In some embodiments, a product is linked to specific content, and insome embodiments, a product is content independent. Specifically, insome embodiments, a product can be linked with and/or connected with oneor several different pieces of content. In one embodiment, for example,a single product may be linked with the first content for a set of firstrecipient-users and the product may be linked with the second contentfor second recipient-users. While the content linked to the productchanges between these two sets of recipient-users, the product remainsthe same. In some embodiments, this different content can be linkedbased on attributes of the recipient-user such as, for example, based ona skill level of the recipient-user, based on a learning style orpreference of the recipient-user, or the like. In some embodiments, oneor several products, each of which can be linked with content, can becombined to form a product model. This can be repeated to createmultiple product models as shown in FIG. 9. FIG. 9 shows products 915-Athrough 915-N, content 990-A through 990-N, and product models 910-Athrough 910-N. Each of the products 915-A through 915-N can be combinedwith at least one content 990-A through 990-N. A combination of products915-A through 915-N linked with content can then be combined to form oneof the product models 910-A through 910-N. In some embodiments, theproducts within a product model can be sequenced according to anadaptive sequencing which can be, for example, authored adaptivesequencing or dynamic adaptive sequencing. In embodiments havingauthored adaptive sequencing, next content can be selected according topredetermined criteria. In embodiments that are dynamically adaptivelysequenced, next content and/or next products can be selected by amachine-learning algorithm trained to recommend products and/or contentbased on a determined user skill level and/or based on a determineddifficulty of the products and/or content. In some embodiments, theproducts within the product model can be linearly sequenced, whichlinear sequencing may be based on, for example, sequencing according toa Table of Contents. In one such embodiment of sequencing according to aTable of Contents chapter 1 section 1 is followed by chapter 1 section 2and then followed by chapter 1 section 3.

Products can intersect with diverse content such as crowdsourced contentand can improve and articulate the content according to contextincluding, for example, recipient-user attributes, with machinelearning. In some embodiments, the product models can sequence contentfor each learner targeted to that learner's needs and learning styles.For example, when the global architecture 810 uses feedback to learnthat a particular student benefits from video stimulus, the productmodel can present a YouTube video having relevant content. Thus, globalarchitecture 810 can be a personalized content delivery system, and thecontent delivered by the global architecture 810 can be any desiredcontent including, for example, educational content, new content,entertainment content, or any other desired content.

The global architecture 810 can provide non-content learning assets or“tools” to create a learning experience. Social and communicationfeatures, allowing connection and collaboration between recipient-users,and between recipient-users and instructors, can facilitate manylearning experiences. In addition, the global architecture 810 itselfcan include, for example, reminder-type and programmatic notificationand announcement functions through a variety of channels including VideoConferencing/multi-participant Video Chat, audio conferencing,whiteboarding, discussions, sharing, social profiles, and text chat.Those channels can include a collaboration function that when coupledwith other features, such as audio and video conferencing, can supportpeer-to-peer collaboration on projects such as writing assignments,homework problems, and a wide variety of platform apps (writingassignments, etc.) and 3^(rd) party tools (e.g. PPT, Word, etc.). Thiscan allow students using self-paced (“asynchronous”) on-line learning togive or get help from other students taking the same course, or studentstaking another type of course to do joint projects. The globalarchitecture 810 can support “push” (that is, from platform to user)notifications through a variety of channels including email, in-browserdelivery, in-App (mobile native app) delivery, and SMS. The globalarchitecture 810 can support an ability for the institution/instructorto customize notification templates, and to configure when notificationsare sent. Notifications may be sent to a configurable set ofparticipants or roles in response to events (e.g. analytic criteria likea student at risk of failure; or homework items being overdue), atdefined times, or asynchronously initiated by an instructor—to provideinformation or otherwise broadcast to a class, section, or set ofstudents. The global architecture 810 can supportrecipient-user-initiated notifications that can permit one or severalstudents to notify a teacher that they do not understand a particularconcept, and to ask the instructor for help.

The global architecture 810 can provide school management features forinstitutions to oversee the effectiveness of their instructors, managestudents and enrolment, and service the needs of the institutionaladministrator. The analytics and adaptive, one of such managementfeatures, are separate though related functions provided by the globalarchitecture 810. The adaptive functionality—that is, the softwarecontrolling real-time student engagement—can leverage the informationproduced by the analytics engine. The analytics engine can maintain andkeep current based on interactive and streamed information, models forefficacy, usage of content for royalty purposes, student mastery oflearning objectives, and many other functions.

Instructional management can be provided by the global architecture 810to the instructor or the institution employing the instructor. Importantinstructional management functionality can include the followingdescribed functionality. A gradebook can be a main dashboard forinstructors, institutions and students to monitor students' progress.While, as the name suggests, grades for quizzes, assessments and coursescan be presented here, the gradebook can also act as a dashboard for aninstitution or instructor to determine how well their students aregrasping the “learning objectives” embodied in the course. The gradebookcan present statistics derived from the student activity streams showinghow long it is taking students to read material, how much of thematerial they have read, demonstrated mastery of specific learningobjectives through assessment questions, how much of any video or otherinteractive content students actually watched, the number of attemptsand time taken to answer questions, and other metrics. In addition,grades for a given quiz or assessment can be computed using algorithmsthat incorporate number of retries, time taken, and other factors. Thegradebook can be used to identify and review systematic causes oferror—for example, adding both the numerator and denominator of afraction, rather than finding a common denominator, when learningaddition of fractions. The view of the Gradebook can vary betweenstudents—who, in some embodiments, only see their own or theirworkgroup's information—and institutions and instructors, who can seeprogress for all their students. An instructor can use this informationduring the course itself to add or re-arrange assignments and materialto address issues his or her class can be experiencing. The Gradebookcan also be used by a teacher (or other person with appropriatepermission, like a teaching assistant) to grade assignments manually.Facilities can be provided to grade group assignments, to incorporateparticipation and other factors into grades, to manually overridegrades, etc. In some embodiments, one feature supporting the gradebookfunctionality can be the “student model”. For a given recipient-user(student), this model can show and/or track to what extent he or she hasdemonstrated mastery of each learning objective. This model can bemaintained algorithmically using an “activity stream” from the reader,taking into account the student's performance (right or wrong responses,etc.) to assessment items and other learning objects (time to read,percent of video watched, etc.) that map to a given learning objective.The student's mastery can be measured in an absolute sense, or relativeto other students who have taken the same assessment items orencountered the same learning objects. A lesson planner supportsinstructor-sequenced lesson plans and is an important feature of theglobal architecture 810. Lesson planning can enable an instructor toorganize content into assignments, and to attach due dates to theseassignments. Assignments can be created for specific individuals, forsections of a larger class, or for a class as a whole. Some features ofthe lesson planner are curriculum management, class management, front ofclassroom management, outcome management, assignment management, andscheduling. These features help instructors schedule events, classes,assignments and book AV and other resources used as a part of theirteaching experience. The system can also make such informationselectively visible to students, such as a calendar, resource booking,as well as due dates.

In some embodiments, the global architecture can provide narrativecontent, assessments, and simulations which can facilitate “learning bydoing” via, for example, practicing new skills and/or demonstratingmastery. In some embodiments, assessments can provide the ability toflexibly ask and answer questions, and flexibly score the responsesbased on a number of scoring criteria. Simulations place recipient-usersin a situation that mimics aspects of “real life,” enabling instructorsand “intelligent agents” to monitor and record the student's performancein near real-time, potentially adapting the scenario to the student'sprevious responses while he or she is still interacting with thesimulation. Simulations may also be scored, either by the instructor, orby an automated system. The line between assessments and simulations issomewhat blurred, because many assessments actually do mimic aspects ofreal-life, while many simulations do set out questions to be answered.For example, one assessment scenario presents a business problem andrequests that a student fill out an actual balance sheet, while an ITsimulation requires that the user create a table in Word. While theremay be a distinction in a pedagogical sense, assessments and simulationsmay both be treated uniformly by the global architecture 810. An“assessment” is student activity that can be scored. The globalarchitecture 810 monitors activity stream and many aspects of thestudent's interaction with the system—including time to read, time torespond. And nearly any activity, including simulations, can be scored.This renders many or most simulations and interactives as assessments.The global architecture 810 supports assessments of several types. Afirst type is a high stakes assessment. These are assessments where theresults have a major impact on a student's course grade—such as amid-term or final exam—or on the issuance of a professional or otherlicense. In general, a much higher degree of security and scrutinysurrounds this type of assessment than other types. In particular, thesetting in which the assessment may be taken—the IP addresses of themachines, and so on—are often controlled. Systems are often “lockeddown”, and physically isolated. Another type is a low stakes assessment.While a recipient-user's performance on quizzes and practice problemsgenerally does have an impact on their grade, these are consideredlow-stakes in the sense that their primary purpose is learning ratherthan assessment. While potential cheating is still a consideration,flexibility, ease-of-use and educational value become the primaryconcerns of a low-stakes assessment. Other types of assessments includediagnostic, administration, psychometric, automated language evaluation,and performance reporting. The global architecture includes item andtest construction tools for the authoring of test questions and answers.These tools provide support for constructing good questions, tying themto a specific learning objective, and assessing their efficacy. Thereare a variety of ways assessment questions potentially interact with theglobal architecture 810. The first is self-contained; score data outputonly. The global architecture 810 launches a self-contained assessmentor simulation when required, but does not set the initial conditions ofthe assessment. The student interacts with the assessment, with thestudent's interaction with that assessment determined totally by theassessment component. The assessment component reports either a rawscore (#retries, #correct, etc.) or a pre-computed score back to theplatform. The sequence in which individual assessments or simulationsare presented to the student are platform controlled in an adaptive orinstructor-determined model. To the global architecture 810, eachassessment question is a “black box”. A second embodiment is when theglobal architecture 810 sets initial conditions and scores data outputonly. In this scenario, the global architecture 810 supplies a list ofparameters to the assessment and determines the specific set ofvariables to be used in the assessment. In this embodiment the “model”defining those parameters is executed on the platform, rather than as anembedded part of a self-contained assessment. The advantage to executingthe model on the global architecture 810 is that the variables drivingthe assessment or simulation can be chosen systematically rather thanrandomly. In particular, these variables can be based on complex factorslike the student's current mastery of the material, as determined by theanalytic models governing the student model. Other embodiments includesending the activity stream data that would allow the globalarchitecture 810 to gather more nuanced data on efficacy and studentbehaviour. Another embodiment is a fully interactive mode, where theplayer sends activity stream and student responses, and the globalarchitecture 810 responds to each interaction.

An important characteristic of the self-contained assessment model inthe “global architecture 810 is that it sets initial conditions and doesnot require instructor interactivity while the question is beinganswered by the recipient-user. From the standpoint of the globalarchitecture 810, such questions can be “fire and forget”—once theassessment or simulation is launched, and initialization of the questionis (optionally) done, the global architecture 810 simply waits for ananswer. While the interaction with the recipient-user appears—and infact is—richly interactive, that interactivity is completely determinedby the initial conditions of the problem, and can therefore beimplemented in a self-contained assessment component. This makes thistype of question very scalable, and useful off-line as well. The wayassessment questions are structured is to some degree a matter of styleand intent. A question could be self-contained, requiring no input; allnumerical values required could be generated within the assessmentquestion itself, and not require initialization by the globalarchitecture 810. At the other extreme, the same question could bedriven entirely by the global architecture 810, with the globalarchitecture 810 itself supplying initial and intermediate values, aswell as doing the scoring. The global architecture 810 can deal withmultiple styles of questions, because the interaction of the globalarchitecture 810 with assessment components can be, in some embodiments,entirely model-driven.

The global architecture 810 offers administration and user features forstudent management functionality to manage student registration forcourses, to publish available courses, and to manage student'sentitlements to courses and course material based on their enrolment.Other administration and user features are on boarding,login/registration, user management, catalog/course listings, andentitlements.

The global architecture 810 provides a reader client that is used by therecipient-user or instructor to consume content, as well as to accessother non-content global architecture 810 features such as notificationsor chat. A reader may be browser-based, mobile-native, or implemented onother technologies. In some embodiments, more than 50 readers can beassociated with the global architecture 810, including, for example,Reader+, eText2, and Revel. Important features of the reader clientsinclude: rendering and layout of narrative content, navigation,glossary, annotations, bookmarks, search, offline operation, anddownloadable content capable.

Learning tools are global architecture 810 features that are offeredindependently of a particular product or learning model. Since theglobal architecture 810 supports multiple learning models packaged inthe same product, learning tools may be either “integrated” or “bundled”with a given product. The distinction is that integrated learning toolsare in-line with the content and other resources of a title, while“bundled” products sit side-by-side, or are available to an instructorto add to a course. The learning tools can include in-classinteractivity and peer-to-peer learning by: students in a classroomsetting notifying the instructor that they do not understand aparticular concept, and asking for help; the instructor viewingtabulated student responses to a quiz in near-real time while in class,so he or she knows where students need help; the teacher deliveringinteractive worksheets and quizzes to students while in class; andteachers creating transient or persistent study groups for assignmentsor in-class discussions. The learning tools also include peer-to-peerlearning for asynchronous online courses. This leverages the globalarchitecture 810 social/communication features to allow students inself-paced learning courses to work with each other in real-time;outside-of-class questions. Additional learning tools include questionsthe instructor assigns for students to work on independently, outside ofclass. In some embodiments, the questions and answers persist so that astudent may return to them later for review. Learning tools can alsoinclude math tools including a set of browser-based teaching aids thatcan be used by instructors interactively when teaching questions. Thesecan include: counters, fractions, geometry, place-value blocks, money,data and graphs, number line, input-output machine, strip diagrams,measuring cylinders, number charts, and pan balance. The learning toolscan include writing tools that allow students to write and uploadassignments, as well as access notes, tips, and resources such as adictionary and thesaurus. The learning tools can also provide featuresfor the instructor such as the ability to create assignment, scorecompleted assignments using grading rubrics, and the ability to providepersonalized feedback to students. The writing tool can also allowautomated essay scoring system using natural language processing and anadd-in to Microsoft Word that can offer coaching tools such as grammarand style checkers and editorial guidance, as well as composition toolssuch as citation tracking, multi-project organization, and note-takingadd-ons. The writing tools can enable sharing, commenting on and gradingof video and textual content in a social-media type paradigm. Theinstructor can, in some embodiments, create and grade assignments, andcomment on video as the video plays. This can be used to score groupassignments, presentations and other content. Students may also createtheir own social-media type profiles.

FIG. 10 is an overview block diagram of the components of the globalarchitecture 810, specifically, FIG. 10 depicts a block diagram overviewof the global architecture 810 connected to applications and enablingservices. Microservices 1020, 1025, 1030, 1035, 1040, and 1045 are theunique services that define the global architecture 810, while the otherservices are cross cutting or enabling services. The global architecture810 is also built on strong foundational layers 1050 of security andprivacy, infrastructure, internationalization and data governance andmanagement. The various microservices in the Platform can be organizedinto the following groups: core services; analytics; enabling services;cross-cutting services; and API management and security.

This global architecture 810 can be highly flexible and provide multiplecapabilities and allow for these capabilities to be reused by different“products” and “learning models” via configuration. This flexibility canallow multiple learning models to be served from the global architecture810, with the option of personalizing the experience for each student.While personalization is not needed in all scenarios, the globalarchitecture 810 and architecture support such an ability. Thearchitecture can be built for “web scale” by supporting horizontalscalability, functional partitioning and data partitioning Thearchitecture can be built primarily on the principles of themicroservices architectural pattern. This pattern can allow eachcomponent to be self-sufficient and scalable on-demand with its own lifecycle management. Another important feature can be the loose couplingbetween microservices that can enable a distributed team working inmultiple locations to work on the business functionality withoutblocking other teams. The architecture of the global architecture 810can also support simultaneous work on “back-end” (framework) and“front-end” (application) functionality that provides for a telescopedimplementation schedule. An additional feature can be that the globalarchitecture 810 can be built as a thin server that can provide a clearseparation between the user interface components and backend services.Furthermore, the system layers can be clearly defined, with access viaAPIs using publish and subscription functionality. The globalarchitecture 810 architecture can make each of the microservices“stateless”, with state being fetched when the microservice isinstantiated. This can allow each microservice to efficiently scalehorizontally, and can also result in a highly available system. Theglobal architecture 810 architecture can have no single point offailure, making it highly scalable as well as highly available. Andbecause of its ability to scale, the architecture can be highlyperformant in both user-facing and API-cantered applications. The globalarchitecture 810 architecture can also support rapid applicationdevelopment principles by limiting the amount of coding required forchanges and increasing re-use of what has been coded before. The globalarchitecture 810 architecture can use ESB (enterprise service bus)-basedservice orchestrator for its connection to third party enablingservices. This can be a part of a SOA (service oriented architecture)architectural pattern, and can provide support for disparatecommunication protocols and data models.

The global architecture 810 can be the encapsulation of the“server-side” components of the overall system. The global architecture810 can be based on the thin server architectural pattern; in someembodiments, it can deliver all its services in the form of web servicesand in some embodiments, there are no user interface components executedon the global architecture 810. The global architecture 810 can befurther decomposed into components. These components can be groupedtogether based on their scope within the overall architecture. Eachcomponent can be a microservice, making the overall architecture fullydecoupled. Each microservice can have fail-over nodes and can scale atits own pace based on the demand for that particular service. This meansthe global architecture 810 has no single point of failure, making theplatform both highly scalable and available. In some embodiments, forexample, the global architecture 810 can comprise a plurality of servers102. In some embodiments, each microservive in the global architecturecan be associated with at least one server 102, and can, in someembodiments, be associated with at least one server 102 functioning as aprimary node and at least one server 102 functioning as a fail-over nodefor that microservice. In some embodiments, for example, eachmicroservice can be embodied on a plurality of servers 102, some ofwhich servers 102 function as primary nodes and execute the functions ofthe microservice under normal operating conditions, and some of theplurality of servers 102 function as fail-over nodes and, in someembodiments, are used only in a failure such as, for example, whentraffic exceeds the capabilities of the primary nodes and/or when one ofthe primary nodes fails. In some embodiments, each microservice can beassociated with a plurality of servers 102 that can be communicatinglycoupled to each other. In some embodiments, some or all of thisplurality of servers can operate as primary nodes and as fail-overnodes. In one embodiment, for example, a portion of a server's 102processing capabilities can be held in reserve to function as afail-over node, whereas other portions of the server's 102 processingcapabilities can operate as the primary node. The global architecture810 architecture provides the capability to build and deploy amicroservice instance for each course-recipient-user combination; thatis, a given student could have one microservice service instance foreach course that student is taking. Because each student interacts withtheir own microservice, this makes the global architecture 810 scale upto the limit of cloud resources available—i.e. near infinity.

Any scalable application stretches itself in the direction of at leastone of the dimensions of a scale cube as shown in FIG. 11. The scalecube has three dimensions X, Y and Z. The x-axis represents scaling anapplication by cloning it. This is sometimes called horizontal scaling.A traditional example of cloning is multiple web server instancesfront-ended by a load balancer. Horizontal scaling means scaling anapplication or service by duplicating the executing code on variousphysical nodes. The y-axis scaling is partitioning the application bysplitting it based on business or other logical groupings of functions.Many traditional e-commerce web applications, for example, were built byhaving one machine (or group of machines) handling payments, another setof machines running the shopping basket, and so on. Next, on the z-axisis scaling an application by data partitioning. Data partitioning alsorequires cloning the code just like x-axis, but in this option eachclone only processes a contextualized sub-set of the data. This improvesthe performance of the application and makes it scalable too, since eachsystem has to do less. “Map-reduce” where each “mapper” only operates ona subset of data is also an example of data partitioning. Referring tothe cube in FIG. 11, if the bottom left corner (“starting point toscale”) is the starting point for application scaling, and the right topcorner (labeled “near infinite scale”) is the ultimate scale to beachieved, then we have to scale through all three dimensions to reachthat point. Maximum scale means that we have to scale by cloning(X-axis), functional partitioning (Y-axis) and data partitioning(Z-axis).

The global architecture 810 architecture scales in all three dimensionsof the cube. Along the x-axis, the services are designed as “stateless”,which means that they can scale horizontally to any level. Along they-axis are the microservices such that the whole application isdecomposed into smaller parts that can be built and deployedindependently to achieve functional partitioning. Along the z-axis isthe fine grained data partitioning achieved by the capability of thearchitecture to decompose itself to a level where it creates amicroservice for each course-recipient-user combination. In this way,the global architecture 810 architecture can achieve near infinitescale; or at least the highest current technology can support.

Returning again to FIG. 10, the global architecture 810 includes an: APImanagement and security component 1015, also referred to herein as anexternal communication module; a learning engagement deliverymicroservice 1020, also referred to herein as an engagement deliverymodule 1020 or as an engagement delivery microservice 1020; a learningexperience composer microservice 1025, also referred to herein as anexperience composer microservice 1025; a learning product buildermicroservice 1030, also referred to herein as a product buildermicroservice 1030; a learning asset provisioning microservice 1035, alsoreferred to herein as a data packaging module 1035 or as a datapackaging microservice 1035; a learning model builder microservice 1040,also referred to herein as a model building module 1040 or as a modelbuilding microservice 1040; a core data services microservice 1045;foundational layers 1050 which can include security and/or privacymodules; a cross-cutting services microservice 1055; an analyticsmicroservice 1060; and a service orchestrator 1065.

The global architecture can interact and/or communication with one orseveral applications 1005 and/or enabling services 1070. The enablingservices component 1070 can form the input to the global architecture810 from other systems or can consume the output of the globalarchitecture 810 for further processing such as Billing, e-Commerce,etc. The service orchestrator 1065 can interact with the enablingservices 1070.

The applications 1005 can include, for example, instructor andinstructional designer applications 1007 and/or student learning clientsand applications 1010. Instructor and instructional designerapplications 1007 and student learning clients and applications 1010 canbe, in some embodiments, the consumers of the global architecture 810APIs. Instructor and instructional designer applications 1007 andstudent learning clients and applications 1010 can, in some embodiments,directly interact with end users, and can, from a global architecture810 perspective, provide a user interface to the various servicesprovided by the global architecture 810.

In some embodiments, the global architecture 810 can distinguish betweenthe global architecture 810 and instructor and instructional designerapplications 1007 and student learning clients and applications 1010. Inparticular, in some embodiments, instructor and instructional designerapplications 1007 and student learning clients and applications 1010 canbe consumers of global architecture 810 services and can be used fornavigation and user interaction. In some embodiments, the instructor andinstructional designer applications 1007 and student learning clientsand applications 1010 do not execute “business logic”, but execute“display logic”. In particular, adaptivity and decisions on thesequencing of content based on dynamic factors can occur on the globalarchitecture 810-side, not on the application-side—with the exception ofthe disconnected case where the recipient-user microservice migrates tothe client. Advantageously, this can mitigate maintainability issues andcan prevent fragmentation of services.

In some embodiments, the instructor and instructional designerapplications 1007 and student learning clients and applications 1010 canbe usable in an off-line mode. In such embodiments, some business logicand content must reside on them, at least temporarily while in thatmode. Specifically, in such embodiments, a unique learner microservice,or copy of the same, can reside on the instructor and instructionaldesigner applications 1007 and/or the student learning clients andapplications 1010, and/or the devices 106, 110 containing the same. Theinstructor and instructional designer applications 1007 and studentlearning clients and applications 1010 may be hosted on multipleplatforms including web browsers, mobile-native and other devices.

In some embodiments, access to the global architecture 810 can bethrough a common set of REST APIs. The global architecture 810 can, insome embodiments, supply data to the instructor and instructionaldesigner applications 1007 and student learning clients and applications1010. In some embodiments, the global architecture 810 does not providerendering of screens or content, but rather those functions areperformed by the instructor and instructional designer applications 1007and student learning clients and applications 1010. The instructor andinstructional designer applications 1007 and student learning clientsand applications 1010 can perform some or all elements of data renderingand presentation, using the widgets and other display assets that areappropriate to the device 106, 110 hosting the instructor andinstructional designer applications 1007 and student learning clientsand applications 1010.

In some embodiments, the global architecture 810 can implement one orseveral processes for the ingestion, curating, and delivery of content.This can include the receipt of content. In some embodiments, at thetime of receipt of content, the content and/or data such as metadataassociated with the content can be converted to a format correspondingto the format of the global architecture 810. This content can undergocontent provisioning by, for example, the learning asset provisioningmicroservice 1035, which can make the global architecture 810 “aware” ofthe content. This provisioning can further include the retrieval andpackaging of metatdata and links associated with content to formresources, which resources can be combined with one or several otherresources to form a learning asset. Some or all of these learning assetscan be associated with one or several configurable palettes ofcapabilities that are available to and/or available in the globalarchitecture 810. These learning assets can then be combined in one orseveral learning models, from which one or several products can becreated.

FIG. 12 is a schematic illustration of one embodiment of the APImanagement and security microservice 1015. The API management andsecurity microservice 1015 can be the doorway to global architecture 810and the components within the API management and security microservice1015 can act as gatekeepers. The API management and securitymicroservice 1015 can include the API gateway 972, the authenticationand authorization component 972, the service and registry component 974,and the router (load balancer) 976. The API gateway 970 can be theexternal endpoint made available to the consumers of the globalarchitecture 810 services, and can encapsulate the businessfunctionality of the overall global architecture 810. The API gateway970 can be a common entry point for some or all clients (desktop,mobile, tablet or hubs) accessing the global architecture 810 services.

APIs used by the API gateway 970 can be business functionality madeavailable over the internet using a standard simple statelessarchitecture over HTTP. These APIs can be REST (Representational StateTransfer) services using JSON (JavaScript Object Notation). The APIs canfollow the constraints of REST services, including having a uniforminterface, being stateless and/or cacheable, and operating usingclient/server protocol. The uniform interface can use a restricted setof verbs (GET, PUT, DELETE, etc.), and/or can use query-stringparameters, bodies and headers that can, for example, fully define theinterface between the client and the services provided by the back-end.The REST or “system” API can connect clients and third partyapplications to the facilities provided by the system back-end, and canprovide access to system functionality, data and services.

In some embodiments, some or all of the APIs in the API management andsecurity microservice 1015 can be stateless from the point of view ofthe client. Specifically, in some embodiments, all “session state”information is not passed along with each call, but can be storedaccessible by the APIs. In such an embodiment, when a client makes arequest for content, this request can be communicated through the APImanagement and security microservice 1015 to one or several othermicroservices 1020, 1025, 1030, 1035, 1040 of the global architecture.This API request can be communicated with the client's current token atthe time the client makes a request. In some embodiments, the token canbe communicated as part of a URI, query-string parameters, body, orheaders. In such an embodiment, the URI can uniquely identify theresource and the body can contain the state (or state change) of theresource or client. After processing performed by the back end, theappropriate state, or one or several relevant piece(s) of state, can becommunicated back to the client via headers, status, and/or responsebody. In some embodiments, all API responses can be cached on the clientdevice. In some embodiments, responses can, implicitly or explicitly,define themselves as cacheable, or not, to prevent clients reusing staleor inappropriate data in response to further requests.

In some embodiments, the API acts as a decoupling agent which decouplesthe client from the global architecture 810. This separation of concernsmeans that clients are not concerned with data storage or the “how”which remains internal to the back-end server-side system. Further, theglobal architecture 810 is not concerned with the user interface or userstate, so that the global architecture 810 can be simpler and morescalable. This allows for evolution of both the client and the globalarchitecture 810 separately as long as the interface governing thecommunication between them remains unaltered.

To support near-real-time analytics against incoming data activitystreams, there is a separate activity stream API 978. The activitystream API 978 can ingest and capture one or several activity streams.In some embodiments, each of these activity steams can identify some orall of the interactions of a user with features, content, and/orcomponents of the global architecture 810. In some embodiments, thisactivity steam can be independent of content provided to the user. Insome embodiments, for example, a user connecting to the globalarchitecture 810 can connect via both the API gateway 970 and theactivity stream API 978. The API gateway 970 can collect content relatedinputs, such as responses to questions, from the user, and the activitystream API 978 can capture these same inputs, as well as additionalinputs. Thus, the activity stream API 978 can collect information thatis at least partially redundant to information collected by the APIgateway 970 and/or the activity stream API 978 can collect moreinformation than is collected by the API gateway 970.

In some embodiments, the activity stream API 978 further distinguishesitself from the gateway API 970 in that the activity stream API 978 doesnot return a response. In some embodiments, the activity stream API 978can be directly exposed to the devices 106, 110 sending information tothe activity stream API 978, and in some embodiments, the activitystream API 978 can be pre-configured to the device at the time ofprovisioning the device. In some embodiments, the activity stream APIdoes not include a service registry, discovery, and/or authorizationoccurring in the activity stream API 978. In some embodiments, this lackof authorization can be achieved as the act of provisioning the device106, 110 enables the device 106, 110 to become known to the activitystream API 978 and a token can be supplied to the device 106, 110 whichallows verification and/or determination of the identity of the device106, 110.

In some embodiments, all events and activities from the device areaccepted by the activity stream API provided the token is supplied. Thetoken is the authentication accepted by the activity stream API 978,which activity stream API 978 can continuously ingest and store streamsfrom as many devices as are connected to the global architecture, whichcan be 1,000 devices, 10,000 devices, 50,000 devices, 100,000 devices,1,000,000 devices, 10,000,000 devices, 500,000,000 devices,1,000,000,000 devices, and/or any other or intermediate number ofdevices. In some embodiments, inputs provided to some or all of thesedevices can be collected by the activity stream API 978 and can be usedto generate events, and specifically can be used to generate a largenumber of events.

In some embodiments, the activity stream API 978 can function to captureor “ingest” activity data at high speed. The activity stream API 978 canthen process the activity data to identify one or several activitiesfrom the activity data for pushing to the global architecture 810 as oneor several events. In some embodiments, activity data can be processedin batch mode to provide a better view of the recipient-user and theirinteractions with the global architecture 810 and/or presented content.In some embodiments, some activities captured in the activity data mayrequire prompt response from the global architecture 810 and suchactivities may be individually processed or not processed in batch.

The authentication and authorization component 972 can utilize aclaims-based authorization approach that can accommodate the requirementto support federated authorization. A claim can be based on a mixture ofinformation such as identity, role, permissions, rights, and otherfactors. Claims-based authorization can also provide additional layersof abstraction that make it easier to separate authorization rules fromthe authorization and authentication mechanism. For example, theauthentication and authorization component 972 can authenticate a userwith a certificate or with username/password credentials and then passthat claim-set to the service to determine access to resources.

In some embodiments, the global architecture 810 itself can be IdentityManagement, Access Management (IDAM) implementation independent. IDAM,Authorization and Entitlements can be treated as enabling servicesprovided through an external toolset and accessed via an enterpriseservice bus. Interaction with external systems can be via a well-definedand simple interface, with the IDAM system itself hiding considerablecomplexity from the global architecture 810. This can allow the globalarchitecture 810 to focus on permitted interactions with authorizedindividuals rather than concerning itself with how the entitlements andauthentication itself is done. The REST API layer can be protected bytoken based authentication and an SSL enabled transport layer. In someembodiments, a user may be required to present a valid token in order toaccess any API.

The service registry and discovery component 974 can use server-sidediscovery, as each instance of the service exposes an API gateway 970with a particular location and version. The global architecture 810 canuse a server-side registry as it can then continuously expose new andupdated services. The service registry and discovery component 974 is adatabase of services, their instances and their locations. Serviceinstances are registered with the service registry and discoverycomponent 974 on start-up and deregistered on shutdown. The serviceregistry and discovery component 974 can be used for external andinternal services and clients of the service and/or routers query theservice registry and discovery component 974 to find the availableinstances of a service.

The router (load balancer) 976 works in conjunction with the serviceregistry and discovery component 974 to ensure that all instances of theAPI are consumed in equal measure. The global architecture 810 can usemany forms of load balancing known to those of skill in the art as roundrobin, weighted round robin, least connections, and weighted leastconnections. The global architecture 810 can also use least pendingrequests. In some embodiments, least pending requests selects the serverwith the least active sessions based on real-time monitoring. Withreference now to FIG. 13, a flowchart illustrating one embodiment of aprocess 820 for hybrid content provisioning is shown. In someembodiments, this process 820 can be for hybrid content provisioning ofremote content stored on a remote content platform such as in theenabling services 1070 and native content stored within the globalarchitecture 810. The process 820 can be performed by the contentdistribution network 100, including, for example, the globalarchitecture 810 as located within the CDN 100.

The process 820 begins at block 822 wherein content information isreceived. In some embodiments, the content information can be receivedby the one or several servers 102 and specifically by the globalarchitecture 810. In some embodiments, the content information can bereceived by one of the microservices of the global architecture 810, andspecifically by the data packaging model 1035. In some embodiments, thecontent information can be associated with content that can be storedassociated with the global architecture 810 where they can be storedremotely from the global architecture 810, and specifically on a remotecontent platform. In some embodiments, the content can be stored in theenabling services 1070, in some embodiments, the global architecture 810can access the content, the global architecture 810 does not havecontrol of the content.

After the content information has been received, the process 820proceeds to block 824 wherein a package-data asset is formed. In someembodiments, the package-data asset can be performed by the datapackaging module 1035. In some embodiments, an asset can comprise two ormore resources. In some embodiments, the packaging of the contentinformation into package-data asset can include the generation of a linkdirecting or pointing to the content associated with the contentinformation and the storing of that link. In some embodiments, that linkand/or the package-data asset can be stored in a database of the datapackaging module 1035, and in some embodiments, the link and/or thepackage-data asset can be stored in one or several databases within theglobal architecture 810.

After the package-data asset has been formed, the process 820 proceedsto block 826 wherein the package-data asset is enriched. In someembodiments, the package-data asset can be enriched by the applicationof metadata relevant to the content associated with the package-dataasset. In some embodiments, this metadata can identify one or severalattributes of the content learned by the package-data asset including,for example, information relating to the rendering and/or presentationof the content link to the package-data asset. In some embodiments, thismetadata can identify the type of the content linked with thepackage-data asset, which type can specify, for example, a file type,such as, a text document, an image, a video, and audio file, or thelike. In some embodiments, the package-data asset can be enriched by thedata packaging module 1035.

After the package-data asset has been enriched, the process 820 proceedsto block 828 wherein a content request is received. In some embodiments,the content request can be received by the at least one server 100including, for example, by the global architecture 810. In someembodiments, the content request can be received by the globalarchitecture 810 via API that is contained in the applications 1005,which API can direct the content request to, for example, the engagementdelivery module 1020.

After the content request is then received, the process 820 proceeds toblock 830, wherein one or several package-data assets requested in thecontent request received in block 828 are identified. In someembodiments, this can include matching information contained within thecontent request to information associated with the package-data assets.This can be performed by, for example, the engagement delivery module1020 through evaluation of a database of the engagement delivery module1020. After the package-data asset the question the content requestreceived in block 828 has been identified, the process 820 proceeds toblock 832 wherein the package-data asset is delivered. In someembodiments, the package-data asset can be delivered to the user device106 and specifically to the user of the user device. In someembodiments, the delivery of the package-data asset can include therendering of the package-data asset and/or control of one or severalmicroservices within the global architecture 810 to render thepackage-data asset and/or to coordinate the control of software and/orhardware modules to deliver the package-data asset.

With reference now to FIG. 14, a flowchart illustrating one embodimentof a process 840 for hybrid content provisioning is shown. In someembodiments, this process 840 can be for hybrid content provisioning ofremote content stored on a remote content platform such as in theenabling services 1070 and native content stored within the globalarchitecture 810. The process 840 can be performed by the contentdistribution network 100, including, for example, the globalarchitecture 810 as located within the CDN 100.

The process 840 begins at block 842 wherein first content information isreceived. In some embodiments, the first content information can bereceived by the one or several servers 102 and specifically by theglobal architecture 810. In some embodiments, the content informationcan be received by one of the microservices of the global architecture810, and specifically by the data packaging model 1035. In someembodiments, the content information can be associated with content thatcan be stored associated with the global architecture 810 where they canbe stored remotely from the global architecture 810, and specifically ona remote content platform. In some embodiments, the content can bestored in the enabling services 1070, in some embodiments, the globalarchitecture 810 can access the content, the global architecture 810does not have control of the content. In one embodiment, the firstcontent information can be associated with first content stored on aremote content platform such as, for example, within the enablingservices 1070.

At block 844, the first package-data asset is formed. In someembodiments, the first package-data asset can be formed by the datapackaging module 1035. In some embodiments, the packaging of the firstcontent information into the first package-data asset can include thegeneration of a link directing or pointing to the first contentassociated with the first content information and the storing of thatlink. In some embodiments, that link and/or the first package-data assetcan be stored in a database of the data packaging module 1035, and insome embodiments, the link and/or the package-data asset can be storedin one or several databases within the global architecture 810.

At block 846, the first package-data asset is enriched. In someembodiments, the first package-data asset can be enriched by theapplication of metadata relevant to the first content associated withthe first package-data asset. In some embodiments, this metadata canidentify one or several attributes of the first content linked by thepackage-data asset including, for example, information relating to therendering and/or presentation of the first content linked to the firstpackage-data asset. In some embodiments, this metadata can identify thetype of the first content linked with the first package-data asset,which type can specify, for example, a file type, such as, a textdocument, an image, a video, an audio file, or the like. In someembodiments, the first package-data asset can be enriched by the datapackaging module 1035.

At block 848, a second package-data asset can be formed and/or enriched.In some embodiments, this can include the receiving of second contentinformation in a similar manner to that described with respect to lock842. In some embodiments, this second content information can relate tosecond content which can be native content to the global architecture810 and specifically can be stored in memory or in a database that canbe, in some embodiments, in or controlled by the global architecture810. After the second content information has been received, the secondpackage-data asset can be formed in the same manner as discussed withrespect to the first package-data asset in step 844. After the secondpackage-data asset has been formed, the second package-data asset can beenriched with metadata associated with the second content. Thisenriching can be similar to the enriching performed in block 846 of FIG.13.

At block 850, the first package-data asset and the second package-dataasset are stored. In some embodiments, these package-data assets can bestored in memory associated with the global architecture. After thestoring of the package-data assets, the process 840 proceeds to block852, wherein a content request is received. In some embodiments, thecontent request can be received by the at least one server 100including, for example, by the global architecture 810. In someembodiments, the content request can be received by the globalarchitecture 810 via API that is contained in the applications 1005,which API can direct the content request to, for example, the engagementdelivery module 1020.

After the content request is then received, the process 840 proceeds toblock 854, wherein one or several package-data assets requested in thecontent request received in block 852 are identified. In someembodiments, this can include matching information contained within thecontent request to information associated with the package-data assets.This can be performed by, for example, the engagement delivery module1020 through evaluation of a database of the engagement delivery module1020.

After the package-data asset requested in the content request receivedin block 852 has been identified, the process 840 proceeds to block 856wherein a type of the requested package-data asset is identified. Insome embodiments, this determination can be performed based on enrichingmetadata associated with the requested package-data asset. This type ofinformation can specify, for example, a file type of the requestedcontent, capabilities for rendering and/or presenting the requestedcontent, or the like.

After the type of the package-data asset has been determined, theprocess 840 proceeds to block 858, wherein the package-data asset isdelivered. In some embodiments this can include the delivery of thefirst package-data asset, the delivery of the second package-data asset,or the delivery of both the first and the second package-data assets. Insome embodiments, the package-data asset can be delivered to the userdevice 106 and specifically to the user of the user device. In someembodiments, the delivery of the package-data asset can include therendering of the package-data asset and/or control of one or severalmicroservices within the global architecture 810 to render thepackage-data asset and/or to coordinate the control of software and/orhardware modules to deliver the package-data asset. In some embodiments,the package-data asset can include metadata and a link to contentassociated with the metadata. In such an embodiment, delivery of thepackage-data asset may trigger the recipient device to automaticallyretrieve the content via the link and/or to display the content to theuser of the recipient device. In some embodiments, this display of thecontent can be according to the rendering of the package-data asset.

After the package-data asset has been delivered, the process 840proceeds to decision state 860, wherein it is determined if anadditional content request is received. If no additional content requestis received, then the process 840 proceeds to block 862, wherein userdata of the user recipient of the package-data asset delivered in block858 is updated. In some embodiments, information relating to thedelivered package-data asset can be further updated. These updates can,in some embodiments, be based on one or several responses received bythe global architecture 810 in response to the delivery of thepackage-data asset. If an additional request has been received, the userdata can be updated like in block 862, after which the process 840 canreturn to block 854 and can continue as outlined above.

With reference now to FIG. 15, a flowchart illustrating one embodimentof a process 930 for redundant content communication is shown. Theprocess 930 can be performed by all or portions of the contentdistribution network 100 including all or portions of the globalarchitecture 810. In some embodiments, the process 930 can be performedby the API management and security microservice 1015, and specificallyby the API Gateway and the activity stream API. The process 930 beginsat block 932 wherein a connection request is received by the globalarchitecture 810 from the user device 106 and/or supervisor device 110.This connection request can include information identifying the userand/or the device 106, 110, including, for example, a username,password, or the like. After the connection request is then received,the process 930 proceeds block 934, wherein the user and/or the device106, 110 is validated. In some embodiments, this validation can includedetermining, based on the received information identifying the userand/or the device 106, 110 is accurate and associated with a currentuser having the required access.

After the user and/or the device 106, 110 have been validated, theprocess 930 proceeds block 936, wherein communicating connection withdevice 106, 110 is generated. After the communicating connection isestablished with device 106, 110, the process 930 proceeds block 938,wherein a connection with first API, and specifically with the APIGateway is established, and the process 930 proceeds to block 940wherein a connection with the second API, and specifically with theactivity stream API is established. In some embodiments, each of thefirst API, and the second API, can receive data from the user device,whereas only the first API provides data to the user device. In someembodiments, for example, the first API can provide content and/orpackage-data assets to the user, and can receive responses and/orrequest for content from the user. In some embodiments, the connectionwith one or both of the first and second APIs can be based on tokensexchanged between the device 106, 110 and the global architecture 810.In one embodiment, for example, the generation of the connection withthe second API can be based on the receipt of a token from the device106, 110 and the validation that the token was received.

After connecting to the first and second APIs, the process 930 proceedsto blocks 941 through 947, and specifically to block 941, wherein anactivity stream is ingested by the second API. In some embodiments, theactivity stream can comprise data indicative of all interactions betweenthe user and the device 106, 110, and/or the user and the globalarchitecture 810. This ingested activity stream can include, forexample, responses and/or information requests or content requests.After the ingestion of the activity stream or while ingesting theactivity stream, the process 930 can proceed to block 943, wherein thesecond API generates event data. In some embodiments, an event data canbe generated from the ingested activity stream.

After event data has been generated, the process 930 proceeds to block945, wherein one or several event data destinations are identified. Insome embodiments, the event data destination can be a destination forgenerating event data, and specifically can be one or several modulesand/or microservices of the global architecture 810 designated toreceive the event data. In some embodiments, for example, the second APIcan include a database, and/or have access to a database identifyingdestinations events and/or of event types. After identification of oneor several event data destinations, the event data can be pushed to theone or several event data destinations. In some embodiments, this caninclude pushing the generated events and/or the generated event data toone or several recipient microservices.

In some embodiments, the process 930 can perform steps 942-946 inparallel with steps 941-947, with steps 941-947 being performed by thesecond API and steps 942-946 being performed by the first API. In someembodiments, this can result in redundant APIs each receivinginformation from the device 106, 110 and each processing thatinformation. At block 942, content can be provided to the device 106,110 via the first API. This content can correspond to actual contentand/or to a package-data asset. After the content has been provided, theprocess 930 proceeds to block 944, wherein a response is received. Thisresponse can be received by the first API, but can also be received bythe first API as a part of the activity stream. After receipt of theresponse, the process 930 proceeds to block 946, wherein the responseand specifically response data received with the response is deliveredto the engagement delivery microservice 1020 and specifically to thelearner microservice associated with the user from which the response isreceived. The delivery of this response data to the learner microservicecan result in the evaluation of the response data and the subsequentidentification and delivery of a next package-data asset to the device106, 110, which can result in the repeating of steps 942-946.

Core Data Services

With reference now to FIG. 16, a schematic illustration of the core dataservices microservice 1045 is shown. The core data services microservice1045 exposes the services that facilitate provisioning the entities thatconstitute the core data model of the global architecture 810, includingthe User Profile, and others. Specifically, core data services 1045sources data from external components like Identity Management, AccessManagement (IDAM), the LMS, and other enabling services, and providesthis data to others of the microservices such as, for example, alearning engagement delivery microservice 1020, a learning productbuilder microservice 1030, a learning asset provisioning microservice1035, and/or a learning model builder microservice 1040. Core dataservices microservice 1045 performs the operation of making the globalarchitecture 810 aware of the existence of this data, and enables it bereferenced when required. While this data may be stored in the globalarchitecture platform, in some embodiments, the data is not stored inthe global architecture platform 810 and in some embodiments, this datais not under control of the global architecture platform 810.

The core data services microservice 1045 includes a master data module1310, and an API 1330. In some embodiments, the master data module 1310can comprise a repository of data for consistent application across oneor several of the modules or components of the global architectureplatform 810. In some embodiments, the master data module 1310 can serveas a source of truth for data contained therein.

The Data Packaging Module

With reference now to FIG. 17, a schematic illustration of oneembodiment of the data packaging module 1035 is shown. The learningasset provisioner microservice 1035 allows provisioning of package-dataassets into the system. In some embodiments, the provisioning of contentcan make the global architecture platform 810 “aware” of content via theprovisioning of that content. This provisioning can include the creationof a package-data asset associated with content and the enriching of thepackage-data asset with metadata associated with that content. In someembodiments, this metadata can specify, for example, a type of contentassociated with the package-data asset, rendering instructions,rendering and/or presentation requirements, or the like. In someembodiments, this metadata that is enriched into the package-data assetcan be used by the global architecture platform 810 to facilitatepresentation of content and to identify rendering and/or software orhardware resources required to render and/or present the content of thepackage-data asset. In some embodiments, a package-data asset can be thesmallest unit of a learning experience.

The data packaging module 1035 can include an asset manager 1405, asemantic enricher 1410, a resource manager 1415, a database 1425, and anAPI 1430. In some embodiments, the API 1430 can be RESTful serviceexposed by the package-data asset provisioning microservice 1035, and insome embodiments, the API 1430 can be a stateless service. The assetmanager 1405 can manage package-data assets 1420 and their validations.The semantic enricher 1410 can extract embedded semantic elements fromcontent during provisioning of resources or provisioning of learningassets 1420. The semantic enricher 1410 can further enrich thepackage-data asset with extracted information as part of package-dataasset metadata. The semantic enricher 1410 can be also used forautomating resource provisioning by extracting the metadata contentembedded in the content at the time of its creation in the contentmanagement system. The resource manager 1415 can control servicesrelated to learning resource entities including the create, retrieve,update and delete operations. In some embodiments, the resource manager1415 can manage data at a resource level.

The data packaging module 1035 can include an asset provisioner 1435.The asset provisioner 1435 can encapsulate the delegation andcoordination of method call to underlying modules. In some embodimentsfor example, the asset provisioner 1435 can provide a façade withinwhich delegation and coordination of method calls of underlying modulescan be coordinated. Thus, in some embodiments, one or severalmicroservices of the global architecture 810 may interact with the assetprovisioner 1435 to access the underlying modules of the data packagingmodule 1035.

With reference now to FIG. 18, a flowchart illustrating one embodimentof a process 950 for automatic generation of a package-data asset isshown. The process 950 can be performed by all or portions of the CDN100 and specifically of the global architecture 810. In someembodiments, the process 950 can be performed by learning assetprovisioning microservice 1035. The process 950 begins at block 952,wherein first content information is received. In some embodiments, thefirst content can comprise metadata associated with one or severalpieces of content. In some embodiments, the first content informationcan be received from the enabling services 1070 when content isnon-native and thus located remotely from the global architecture 810,and in some embodiments, the first content information can be receivedfrom memory associated with the global architecture 810 such as, forexample, the database server 104 and specifically the content librarydatabase 303. In some embodiments, the first content information canidentify a location of the content associated with the first contentinformation, which location information identifying a location cancomprise a URL, directory path, or the like.

After the first content information is received, the process 950proceeds to block 954, wherein the received content information isparsed. In some embodiments, this parsing can be performed according toa natural language processing algorithm. After the content informationis parsed, the process 950 proceeds to block 956 wherein semanticelements are extracted from received first content information. Theseextracted semantic elements can, for example, identify capabilitiesrequired for presentation of the content associated with the receivedfirst content information.

After the semantic elements have been extracted, the process 950proceeds to block 958, wherein a first package-data asset is generated.In some embodiments, the generating of the first package-data asset caninclude the packaging of a link directing to the location of the contentassociated with the first package-data asset. This can include, forexample, the packaging of a URL, a directory path, or the like. Afterthe first package-data asset is generated, the process 950 proceeds toblock 960, wherein the first-package data asset is enriched. In someembodiments, the package-data asset can be enriched with metadataassociated with the content and/or relating to the content. In someembodiments, this enrichment can be performed based on and/or with thesemantic elements extracted from the received content information. Theenriched first package-data asset can be stored in, for example, adatabase associated with the learning asset provisioning microservice1035 such as, for example, the learning asset store.

The Model Building Module

With reference now to FIG. 19 a schematic illustration of one embodimentof the learning model builder microservice 1040, also referred to hereinas a model building module 1040 or as a model building microservice 1040is shown. The model building microservice 1040 can identify and/ordetermine capabilities, including a configuration of capabilitiessupported by the global architecture 810 to deliver a learningexperience. In some embodiments, this can include identifying and/ordefining parameters that can be used to score and/or dynamically providecontent to support learning and specifically to support personalizedlearning. In some embodiments, these parameters can be computed “live”at runtime by the Analytic subsystem, using the particular computationalmodels specified by the model building microservice 1040. In someembodiments, the model built by the model building microservice 1040 canprovide the link between computational analytics and the student'sexperience.

In some embodiments, this learning model builder microservice 1040 canprovide the services to configure the capabilities to build a learningmodel. This learning model can comprise the aggregation of capabilitiesand/or configurations of capabilities for use in connection with one orseveral package-data assets. The determination of capabilities and/orconfiguration capabilities can include determining the capabilitiesand/or configuration capabilities needed to render and/or providecontent associated with existing package-data assets. This determinationcan include the retrieval of metadata enriched into package-data assetsand extracting information from this metadata identifying, for example,the content type and/or capability requirements for the rendering and/orpresentation of content associated with that package-data asset. In someembodiments, determining of capabilities and/or configurationcapabilities can include determining the capabilities and/orconfiguration capabilities available to the global architecture 810.This can include, for example, retrieving information identifyingsoftware, drivers, apps, or the like existing within the globalarchitecture 810 and the file types, formats, rendering, or the likeenabled by those drivers, apps, softwares, or the like.

The model building module 1040 can include: an API 1630, a capabilitiesmanager 1605, one or several asset filters 1610, a constraints manager1615, a database 1625, and a model builder 1635. The API 1630 can be aRESTful service exposed by learning model builder microservice 1040. Insome embodiments, the API 1630 can be a stateless service. Thecapabilities manager 1605 can manage the list of capabilities that aresupported by the global learning platform 810. In some embodiments, thecapabilities manager can provide methods to create, retrieve, update anddelete the capabilities. Specifically, this can include tracking some orall of the capabilities of the global architecture 810. This can includeidentifying capabilities of some or all of the microservices that are apart of the global architecture 810 and/or are associated with theglobal architecture. In some embodiments, this can include capabilitydiscovery of capabilities of those microservices of the globalarchitecture 810. The asset filters 1610 can capture and manage thelearning models 1620 that can be specific to a set of learning assets1420. In some embodiments, the asset filters 1610 link capabilities andassets. For example, a product model may include one or severalcapabilities that define applicability of content and/or assets to thatproduct model. In some embodiments, the asset filter can filter assetsto match one or several capabilities of one or several product models.The constraints manager 1615 can capture and manage the constraints onthe learning models 1620 that can be very specific to geography anddemography of the learner. In some embodiments, these constraints can bebased off of, for example, a geographic location in which the productmodel is being used, the legal environment in which the product model isbeing used, capabilities of the product model, or the like. Modelscreated by the learning model builder microservice 1040 can be stored inthe database of the learning model builder microservice 1040.

The model builder microservice 1040 can include the model builder 1635.The model builder 1635 can encapsulate the delegation and coordinationof method call to underlying modules. In some embodiments for example,the model builder 1635 can provide a façade within which delegation andcoordination of method calls of underlying modules can be coordinated.Thus, in some embodiments, one or several microservices of the globalarchitecture 810 may interact with the model builder 1635 to access theunderlying modules of the model builder microservice 1040.

With reference now to FIG. 20, a flowchart illustrating one embodimentof a process 1500 for redundant content communication is shown. Theprocess 1500 can be performed by the global architecture, andspecifically by the model building module 1040. In some embodiments, theprocess can be performed as a part of the execution and/or delivery of amodel. The process 1500 includes three components or sub processes thatcan be simultaneously or serially performed. In some embodiments, thefirst sub process comprising blocks 1502 through 1506 can be performedby the capabilities manager 1605, the second sub process comprisingblocks 1508 through 1512 can be performed by the constraints manager1615, and the third sub process comprising blocks 1514 through 15, fourcan be performed by the asset filter 1610, and/or the model builder1635. The process 1500 can begin at block 1502, wherein one or severalservices, microservices, and/or modules of the global architecture 810are identified. After these devices are identified, the process 1500proceeds to block 1504, wherein capability information is retrievedthese identified services, microservices, and/or modules for the globalarchitecture 810 and/or for some or all of the identified devicescoupled to the global architecture 810. This capability information canidentify hardware and/or software capabilities. In some embodiments,this retrieving of information regarding capabilities can be performedby the capabilities manager 1605.

After the capability information has been retrieved, the process 1500proceeds to block 1506 wherein capabilities are identified. In someembodiments, this can include generating a capability database. In someembodiments, the capability database can include data identifyingcapabilities of the global architecture and/or of devices coupled to theglobal architecture. The capability database can be generated and/ormaintained by the capabilities manager 1605, which can periodicallyreview the capabilities database for accuracy and/or update thecapabilities database. The capabilities database can be stored in thedatabase 1625.

At block 1508 of the process 1500, user data is retrieved. In someembodiments, this user data can be for a plurality of users. In someembodiments, this can include information relating to locations ofusers, legal jurisdictions in which users are located, user demographicinformation, or the like. After the user data has been retrieved, theprocess 1500 proceeds to block 1510 wherein relevant constraintsidentified based on the retrieved user data. In some embodiments, theserestraints can include restraints on personal information, restraints onallowable content and/or learning assets, pricing information, or thelike. After the relevant constraints been identified, the process 1500proceeds block 1512 wherein a constraint databases generated. In someembodiments, the constraint database can be generated by the constraintsmanager 1615, and can be stored in the database, 1625 of the modelbuilding module 1040. In some embodiments, the constraints manager canmaintain the constraints database and can periodically review theconstraints database to determine whether an update is desired, and/orneeded.

At block 1514 of the process 1500, one or several potential data assetsare identified. In some embodiments, these potential data assets can bepackage-data assets that can, for example, of the data packaging module1035. These one or several assets can be identified by the model builder1635. After one or several potential data assets have been identified,the process 1500 proceeds block 1516 wherein one or several assetfilters are identified and/or retrieved. In some embodiments, theseasset filters can be used to restrict all potential package-data assetsto a desired set or subset of package-data assets. The asset filters canbe retrieved by the asset filter 1610 from, for example, the database1625 of the model building module 1040.

After the asset filters a been retrieved, the process 1500 proceeds toblock 1518, wherein the potential package-data assets are filtered bythe asset filters to identify a subset of package-data assets. After thesubset of package-data acids is been identified, the process 1500proceeds to block 1520 wherein capability data is retrieved and/orapplied to the subset of package-data assets. The capability can beretrieved from the capabilities manager 1605 and/or the database 1625 ofthe model building module 1040, and can be applied to the subset ofpackage-data assets by the model builder 1635. In some embodiments, theapplication of capabilities to the package-data assets can enable and/orfacilitate rendering of the package-data assets.

A block 1522 constraints are retrieved and applied to the subset ofpackage-data assets. In some embodiments, these constraints can beretrieved from the constraints manager 1615 and/or the database 1625 ofthe model building module 1040, and can be applied to the subset ofpackage-data assets by the model builder 1635. In some embodiments,these constraints can apply limits on content provisioning, rendering,capabilities, information sharing transmission, or the like. After theretrieval and application of constraints, the process 1500 proceeds toblock 1524 wherein a model comprising the subset of package-data assetswith applied constraints and/or capability data is stored. The model canbe stored in the database 1625 of the model building module 1040.

In some embodiments, this model can be provided and/or accessed

The Product Builder and Experience Composure Modules

With reference now to FIG. 21, a schematic illustration of the learningproduct builder microservice 1030, also referred to herein as a productbuilder microservice 1030 or a product builder module 1030 is shown. Thelearning product builder microservice 1030 provides services to buildproducts based on the package-data assets and the learning model.Specifically, the learning model product builder microservice 1030 formsa product by the combination of a plurality of package-data assets andone or several learning models associated with those package-dataassets. In this built product, the package-data assets can be the meansto achieve a learning goal or learning objective, also referred toherein as an objective, of the product. Learning products, once created,are packaged into “Product Offerings.” The global architecture 810 canprovide products based on a semantically enriched content graph thatenables personalization while retaining pedagogical control and fromthere to dynamically adaptive, model-driven titles. Together with thelearning experience composer, the learning product builder offers apowerful options to instructional designers.

The product builder 1030 can include an API 1830, an asset filter 1805,a learning model validator 1810, a learning model manager 1815, adatabase 1825, and a product builder 1835. API 1830 can be the RESTfulservice exposed by the learning product builder microservice 1030, andit can be a stateless service. The asset filter 1805 can filter thepackage-data assets for products 1820. In some embodiments, the assetfilters 1805 link capabilities and assets. For example, a product modelmay include one or several capabilities that define applicability ofcontent and/or assets to that product model. In some embodiments, theasset filter can filter assets to match one or several capabilities ofone or several product models. Each product 1820 can, in someembodiments, be associated with at least one learning model. Thelearning model validator 1810 can ensure that the sequencing of thepackage-data assets in the product follows the guidelines set by thelearning model. In some embodiments, the learning model validator 1810of the product builder 1030 can verify that the combined package dataassets and leaning models follow one or several guidelines associatedwith the leaning model. Learning model manager 1815 can be responsiblefor managing the learning model related functions within the learningproduct builder microservice 1030. The product builder 1835 can create aproduct 1820 by assembling the information from various other modules ofthe learning product builder microservice 1030.

With reference now to FIG. 22, a schematic illustration of the leaningexperience composer microservice 1025, also referred to herein as theexperience composer module 1025, as the learning experience composer1025, or as the experience composer microservice 1025 is shown. Theexperience composer module 1025 facilitates building a course from aproduct and assigning a path (content model) to the earning assets. Theexperience composer module 1025 can include: an API 2030, a learningasset manager 2005, a product manager 2010, a product validator 2015, alearning model manager 2035, a database 2025, and course composer 2040.The API 2030 can be a RESTful service exposed by the leaning experiencecomposer microservice 1025, and it can be a stateless service. Thelearning asset manager 2005 can be responsible for searching andmanaging leaning assets 1420. The learning asset manager 2005 can alsofacilitate the creation of user-defined leaning assets 1420 on the flyduring composing of the course 2020. The product manager 2010 can beresponsible for managing the product 1820 related service required bythe learning experience composer microservice 1025. Courses 2020 can bebuilt on the products 1820 offered by the global architecture 810.Product validator 2015 can be the validation engine to confirm that thecourse is following the structure and guidelines defined by itsunderlying product(s) 1820. The leaning model manager 2035 can provideaccess to the information related to the leaning model(s) 1620 definedin the product 1820 and used for course 2020 creation. The coursecomposer 2040 can be the module that assembles the course and makes itready for delivery. In some embodiments, the course composer 2040 cancoordinate and/or interact with other modules of the experience composermodule 1025 to generate one or several courses 2020.

With reference now to FIG. 23, a flowchart illustrating one embodimentof a process 2300 for automatic generation of a hybrid content graph isshown. The process 2300 can be performed by all or portions of theglobal architecture 810 including, for example, the product builder 1030and/or the experience composer 1025. The process 2300 begins a block2302 wherein a set of package-data assets are identified. In someembodiments, the set of package-data assets can be identified in thedatabase 1425 of the data packaging module 1035. The set of package-dataassets can be identified by the product builder 1030. After the set ofpackage-data assets has been identified, the process 2300 proceeds toblock 2304 wherein a content model is retrieved. In some embodiments,the content model can be retrieved from the database, 1625 of the modelbuilding module 1040. The content model can be retrieved by the productbuilder 1030.

After the content model has been retrieved, the process 2300 proceeds toblock 2306 wherein one or several asset types are identified. In someembodiments, for example, the package-data asset can comprise one orseveral content-type package-data assets and/or one or severalnon-content-type package-data assets. In some embodiments, the type of apackage-data asset can be identified and/or determined based on metadataassociated with the package-data asset. This metadata can be retrievedfrom the database 1425 of the data packaging module 1035 simultaneouswith retrieval of the package-data assets forming the set ofpackage-data assets and/or at a time separate from the retrieval of theset of package-data assets. In some embodiments, the identifying of oneor several asset types can include identifying a first group includingsome or all of the assets in the set of set of package-data assets ashaving a first asset type and identifying a second group including someor all of the assets in the set of package-data assets as having asecond asset type. In some embodiments, the first group can include someof the package-data assets in the second group, and in some embodiments,none of the package-data assets in the first group are in the secondgroup. The type of a package-data asset can be identified and/ordetermined by the product builder 1030 based on metadata associated withthe package-data assets.

After the asset type of the package-data assets in the set ofpackage-data assets has been identified, the process 2300 proceeds todecision state 2308. At decision state 2308, one of the package-dataassets from the set of package-data assets is selected and it isdetermined whether the selected package-data asset is a content-typedata asset. In some embodiments, this determination of decision state2308 can be made for some or all of the package-data assets in the setof package-data assets. If it is determined that the package-data assetis a non-content type package-data asset, then the process 2300 proceedsto block 2309, wherein the non-content type package-data asset(s) arestored. In some embodiments, the non-content type package-data asset(s)can be stored in, for example, the database 1825 of the product buildermodule 1030.

Returning again to decision state 2308, if it is determined that thepackage-data asset is a content-type package-data asset, then theprocess 2300 proceeds to block 2310 wherein a sequencing type isdetermined. In some embodiments, the sequencing type is determined basedon one or several inputs received from a user. In some embodiments, thisuser can control all or portions of the generation of the hybrid contentgraph. In some embodiments, for example, a user input specifying asequencing type can be stored in association with it to be generatedcontent graph. In some embodiments, the sequencing type can identify atleast one of adaptive sequencing, non-adaptive sequencing, and partiallyadaptive sequencing

After the sequencing type has been identified, the process 2300 proceedsto decision state 2312 wherein it is determined whether the sequencingtype is adaptive. In some embodiments, for example, a sequencing ofcontent can be adaptive, can be non-adaptive, or can be partiallyadaptive. In embodiments in which the sequencing of content is adaptive,next content can be recommended according to one or several models.These one or several models can be the machine learning models oralgorithms that can be trained to select next content based on inputswhich can, for example, identify attributes of a user or attributes ofcontent. In some embodiments, the attributes of the user can include,for example, user skill level, a user preference, or the like, andattributes of content can include, for example, content difficulty,content discrimination, or the like. In embodiments in which sequencingof content is non-adaptive, next content can be selected according toone or several rules and/or according to prerequisite relationships. Inone embodiment of non-adaptive sequencing, content can be sequenced in aknowledge graph completion of one node in the knowledge graph results inpresenting of a next node in the knowledge graph to the user. A hybridknowledge graph can sequence content in both adaptive and thenon-adaptive manner. In some embodiments, for example, some nodes may belinked in prerequisite relationships and advancements through thosenodes can be according to a predetermined non-adaptive sequencing,whereas other nodes in the knowledge graph may be adaptively linked suchthat advancements through those adaptively and linked nodes is accordingto one or several adaptive models.

If it is determined that the sequencing type is non-adaptive, then theprocess 2300 proceeds to block 2314, wherein sequencing information isretrieved or received. In some embodiments, this sequencing informationcan be retrieved from, for example, the database 1425 of the datapackaging module 1035, from the database 1625 of the model buildingmodule 1040, from the enabling services 1070 including, for example,from the LMS 2810 or from the content platform 2820, or from a userinput. In some embodiments, the sequencing information can correspond toa sequencing of content such as, for example, a table of contents of acontent aggregation can provide sequencing information for content inthat content aggregation

Returning again to decision state 2312, if it is determined that thesequencing type is adaptive, then the process 2300 proceeds to blocks2316 and 2318, wherein the adaptive sequencing is generated. Asspecifically depicted in FIG. 23, at block 2316, content skill areidentified. In some embodiments, for example each package data asset canbe associated with one or several skills, and identifying the contentskill can include determining or identifying this association betweenpackage-data asset and skill. In some embodiments, for example, eachpackage-data asset can have metadata which can contain informationidentifying skill associated with that package data asset. Identifyingthe content skill of a package data asset can include retrieving thismetadata and extracting information identifying the skills of thepackage data asset from the metadata.

After the content skills have been identified, the process 2300 proceedsto block 2318, wherein prerequisite relationships between the skills areidentified. In some embodiments, this can be performed via an iterativestatistical analysis, based on empirical or gathered data, or the like.The prerequisite relationships between skills can be, in someembodiments, identified by the product builder module 1030, andspecifically by the learning model validator 1810 or the learning modelmanager 1815.

After the prerequisite relationship between the skills are identified,or alternatively after the sequencing information is received in block2314, the process 2300 proceeds to block 2320, wherein the package-dataassets are sequenced. The package-data assets can be sequenced accordingto the retrieved sequencing information and/or the prerequisiterelationships between skills identified in block 2318. The package-dataassets can be sequenced by the product builder 1030 and/or theexperience composer module 1025.

After the package-data assets have been sequenced, the process 2300proceeds to block 2322, wherein the sequenced content-type package-dataassets are linked with any non-content type package-data assets. In someembodiments, this can include extracting stored non-content package-dataassets from the database in which they were stored in block 2309. Insome embodiments, this can include identifying and/or generating linksbetween the package-data assets.

After the non-content type package-data assets have been linked with thecontent-type package-data assets, the process 2300 proceeds to block2324, wherein a graph-based representation of the sequenced assets isgenerated. In some embodiments, this can include the populating of agraph-based representation of the package-data assets with nodesrepresenting the package-data assets and the linking of thesepackage-data assets in hierarchical relationships via a plurality ofedges. In some embodiments, the graph-based representation can be storedin the global architecture 810, and specifically one or both of thedatabases 1825, 2025.

The Engagement Delivery Modules

With reference now to FIG. 24, one embodiment of the learning engagementdelivery microservice 1020 is shown. The learning engagement deliverymicroservice 1020, also referred to herein as an engagement deliverymodule 1020 or as an engagement delivery microservice 1020, cansubscribe the recipient-users to a course and can build a logicalmicroservice (recipient-user microservice, also referred to herein as alearner microservice) for each recipient-user and course combination.The recipient-user microservice instance created for that student andcourse can be the interaction point between the player device and theglobal architecture 810 to actually deliver the course and learningexperience. Learner microservices can be self-contained microservicesand can be deployed locally on the global architecture 810 or remotelyon user or supervisor devices 106, 110. While deployed on the globalarchitecture 810, they can use the existing infrastructure toauthenticate and authorize the recipient-users, however when deployedremotely. The learner microservices could use their own authenticationand authorization mechanism such as, for example, an authentication andauthorization module 2405 to ensure the security of the servicesaccessed.

The learning engagement delivery microservice 1020 can include: an API2230, an analytics data manager 2205, a course data manager 2210, arecipient-user data manager 2215, also referred to herein as a learnerdata manager 2215, an instructors data manager 2220, a database 2225,and a microservices builder 2235. The API 2230 can be the serviceexposed by the learning engagement delivery microservice 1020 to enrollrecipient-users and build recipient-user microservices 2250, and it canbe a stateless service.

The analytics data manager 2205 can extract data from the distributedcentral store for analytics data for a given recipient-user(s),instructor(s) and course(s). This can include data for evaluatingreceived responses to determine the correctness of a received responseand/or the degree of correctness of a received response. The analyticsdata manager 2205 can keep the data available to be used in buildingrecipient-user microservices 2250.

The course data manager 2210 can retrieve the data related to a specificcourse, and specifically, this information can be retrieved from one orseveral databases associated with the global architecture 810 such as,for example, the database 1825 of the learning product builder 1030, orthe database 2025 of the learning experience composer 1025.

The recipient-user data manager 2215 can manage the recipient-userrelated services in the learning engagement delivery microservice 1020.This can include, for example, identifying features, services, and/orcapabilities needed for the receipt of content by a user and theproviding of these features, services, and/or capabilities to themicroservices builder 2235 for user is creation of a user microservice2250. This information regarding features, services, and/or capabilitiescan be retrieved from other modules 1055, 1040, 1035, 1030, 1025 of theglobal architecture 810.

The instructors data manager 2220 can make instructor's data availablein the learning engagement delivery microservice 1020. The microservicesbuilder 2235 can build the recipient-user microservices 2250.Specifically, in some embodiments, the microservices builder 2235 canbuild the user microservices 2250 by communicating with and/orcontrolling other modules 2205, 2210, 2215, 2220, 2225 of the engagementdelivery microservice 1020. The microservices builder 2235 canconsolidate the data from analytics data manager 2205, course datamanager 2210, recipient-user data manager 2215, instructor data manager2220, and the database 2225 into a single schema for recipient-usermicroservices. In some embodiments, microservices builder 2235 can beinvoked every time a recipient-user enrolls in a course and can buildthe recipient-user microservice 2250 and can keep that recipient usermicroservice 2250 ready for deployment. In some embodiments, thatrecipient user microservice 2250 and/or a record of that recipient usermicroservice 2250 can be stored in the database 2225, which database2225 is also referred to herein as the custom microservice database2225. In some embodiments, for example, the microservices builder 2235can, upon receive a request for content delivery to a user, determine ifa recipient user microservice 2250 has already been generated for theuser and/or has already been generated for the combination of the userrequesting content and course to which the requested content belongs. Ifsuch a microservice is not identified in the database 2225, then themicroservice builder 2235 can create the customized microservice 2250and can update the database 2225 to include information relating to thismicroservice 2250 and/or to include the microservice 2250.

In some embodiments, the microservice builder 2235 can further create asandbox for each created user microservice 2250 such that each usermicroservice 2250 can operate within a self-contained environment tolimit security vulnerability of the global architecture. In someembodiments, the microservice builder 2235 can register therecipient-user microservices 2250 with the service registry defined inthe API management and security 1015.

With reference now to FIG. 25, a schematic illustration of oneembodiment of the user microservice 2250 is shown. In some embodiments,the user microservice 2250 can comprise a platform user microservice2250-A stored as part of the global architecture 810 and a local usermicroservice 2250-B stored as a local copy on the user device 106. Theseversions 2250-A, 2250-B of the user microservice 2250 can facilitateonline (connected) or offline (unconnected) user interaction with theglobal architecture 810 and/or content thereof.

In some embodiments, a recipient-user might service 2250 can include aplurality of modules and/or components. These can include, for example,platform synchronizer 2240. In instances in which the user device 106 isunconnected to the global architecture 810, the platform synchronizer2240 can synchronize an instance of the recipient-user microservice 2250on the user device 106, the local user microservice 2250-B, with aninstance of the recipient-user microservice 2250, the platform usermicroservice 2250-A, on the global architecture 810. In embodiments inwhich the user device 106 is connected with the global architecture 810,the platform synchronizer 2240 can buffer the activity processed throughthe user microservice 2050 until it can be persisted and processed bythe client.

The recipient-user microservice 2250 can further include a database2435, a recommendation engine 2415, a learning asset retriever 2420,scoring engine 2425, a learning asset sequencer 2410, and/or theauthentication and authorization module 2405. In some embodiments, thedatabase 2435 can store data, such as one or several package-data assetsfor presentation to the user and/or data for sequencing the same. Insome embodiments, this data for sequencing the same can include data foruser by the scoring engine 2425 in evaluating any responses received toprovided package-data assets and/or data, such as one or several models,for use by the recommendation engine 2415 in selecting and/orrecommending next content and/or next package-data assets for presentingto the user.

In some embodiments, the learning asset sequencer 2410 can identify andprovide a next package-data asset. In some embodiments, for example, thelearning asset sequencer 2410 can identify a next learning asset whenthe sequencing of the learning assets is non-adaptive. In such anembodiments, the learning asset sequencer 2410 can identify completedlearning assets in the knowledge graph containing learning assets, canidentify the most recently traversed package-data asset, and canidentify a next package data asset. In some embodiments, the nextpackage-data asset can be the package-data asset that is a child of themost recently traversed or completed package-data asset. The learningasset sequencer 2410 can further maintain and persist user data and/orthe state of a recipient-user for a given course. The learning assetsequencer 2410 can act as a façade to retrieve one or several nextpackage-data asset by internally invoking one or several modules of theglobal architecture 810 and/or of the user microservice 2250.

The recommendation engine 2415 of the learner microservice 2250 canprovide the next package-data asset for a learner to traverse.Particularly, the recommendation engine 2415 can identify one or severalnext package-data assets according to an adaptive recommendation whichcan be, for example, based on one or several machine-learning algorithmstrained to predict and/or identify next package-data assets. In someembodiments, these one or several next package-data assets can bepredicted and/or identified according to one or several attributesand/or features which can be inputted into the machine learningalgorithm. These attributes and/or features can include, for example,score, user skill level, package-data asset difficulty level, time ontask, course objective, learning objective, or the like. In someembodiments, the user skill level can be specific to the user to whomthe package-data asset is to be provided, and in some embodiments, theuser skill level can include attributes of other users in, for example,a group such as a class with the user to whom the package-data asset isto be provided. In some embodiments, the recommendation provided by therecommendation engine 2415 can be based on a learning model associatedwith that particular course, which learning model can be retrieved bythe user microservice 2250 from the model builder module 1040.

The learning asset retriever 2420 fetches the package-data asset from adatabase 1425 of the learning asset provisioning microservice 1035,which database 1425 is also referred to herein as the learning assetstore 1425. In some embodiments, the learning asset retriever 2040 canpre-fetch package-data assets and cache them in the database 1425 of thelearner microservice 1035 thereby improve response. Further, to supportan off-line mode, the learning asset retriever 2420 can also downloadall package-data assets associated with the course, and maintain them inits cache or in the database 2435.

The scoring engine 2425 contains and/or can access algorithm to measurethe performance of the recipient-user, and specifically to evaluateresponse received from the recipient-user and/or interaction by therecipient user with the global architecture 810. In some embodiments,the scoring engine 2425 can compute the score of a learning asset 1420of type “assessment” based on the response from the recipient-user. Thescoring engine 2425 can also compute one or several scores of learningasset 1420 of type “narrative” based on the recipient-users interactionwith the learning asset 1420 during the process of learning.

The user microservice 2250 can further include the API 2430. The API2430 can be the service interface for the recipient-user microservice2250. The API 2430 can be stateful such that each subsequent requestwill fetch a response based on the response of previous request.

With reference now to FIG. 26, a flowchart illustrating one embodimentof a process 900 for automated personalized microservice generation isshown. The process 900 can be performed, for example, when content isrequested and a personalized microservice has not been previouslygenerated and/or assigned. The process 900 can be performed by thecontent distribution network 100, including, for example, the globalarchitecture 810 as located within the CDN 100.

The process 900 begins at block 902, wherein a communicating connectionwith a user device 106 is established. In some embodiments, thiscommunicating connection can be established via one or severalcommunications APIs contained within the API management and securitymicroservice 1015. In some embodiments, the establishment of acommunicating connection can include the receipt of a unique useridentifier such as, for example, a username and/or password. At block904 a content request is received. The content request can be receivedby the server 102 and specifically by the global architecture 810 fromthe user device 106. The content request can include, for example,identification of the requesting user, identification of the requestedcontent, capability information identifying one or several attributesand/or capabilities of the user device 106, a content group, or thelike.

After the content request has been received, the process 900 proceedsblock 906 wherein the microservice sub-database, which can be a part ofthe user profile database 301 of the database server 104 and/or part ofthe database 2225 is queried. In some embodiments, this query cancomprise a request to determine whether the microservice sub-databasecontains a custom user microservice corresponding to the receivedcontent request. In some embodiments, this can include determiningwhether a custom user microservice is already generated for the userfrom which the content request is received and/or whether a custom usermicroservice is already generated for the combination of user in acourse for which the content request is received.

At decision state 908, it is determined if the microservice sub-databasecontains the query for microservice. If it is determined that themicroservice sub-database does not contain the query for microservice,then the process 900 proceeds to block 910 wherein a content group forthe requested content is identified and/or retrieved, and/or data forthe content group is identified and/or retrieved. In some embodiments,the content group can correspond to the course in which the user isenrolled. In some embodiments, the content group can be identified basedon information stored in the database server 104, and specifically inthe user profile database 301 of the database server 104.

Once the content group has been identified and/or retrieved, process 900proceeds to block 912 wherein one or several capability requirements forcontent associated with that content group are determined. In someembodiments, this can include determining capability requirements of therequested content and/or subsequent content to that requested content,which subsequent content can be presented to the user based on userinteraction with the global architecture 810. In some embodiments,capability requirements can be stored in the global architecture 810.Specifically, in a database associated with, for example, the learningexperience composer 1025, the learning product builder 1030, learningasset provisioning 1035, and/or the learning model builder 1040. Thesecapability requirements are to identify required capabilities of theuser device for receipt of content associated with the content group,capability requirements for custom user microservice, or the like.

After the capability requirements are identified, the process 900proceeds block 914, wherein one or several microservice modulesidentified. In some embodiments, these include, for example,authentication and authorization module, a learning asset sequencer,recommendation engine, a learning asset retriever, scoring engine, thedatabase, and/or platform sequencer. These modules can be identifiedfrom one or several databases of the engagement delivery microservice1020.

After the microservice modules have been identified, the process 900proceeds to block 916, wherein microservice module data is receivedand/or retrieved. In some embodiments, this information can comprisecode or code segments to which one or several microservice modules canbe created. This microservice module data can be received to retrievedfrom one of the databases of the engagement delivery microservice 1020such as, for example database 2225. After the microservice module datahas been received or retrieved, the process 900 can proceed to block918, wherein the custom user microservice is created. In someembodiments, the custom user microservice can be created by themicroservice builder 2235 of the engagement delivery microservice 1020based on the identified capability requirements, the identifiedmicroservice modules, the retrieved content groups, and the retrievedmicroservice module data. In some embodiments, the custom usermicroservice can include, for example, a recommendation engine which canbe an independent recommendation engine and which can comprise amachine-learning algorithm that can be trained to identify next contentbased on one or several attributes of the next content and/or of theuser.

After the microservice has been created, or returning to decision state908, if it is determined that there is an existing microservice, thanthe process 900 proceeds to block 920 wherein microservices launched. Insome embodiments, the launch of the service can include the generationof the graphical user interface associated with microservice, thetransfer of data to the user device 106 and/or the supervisor device110, and/or the downloading of the microservice to the user device 106or supervisor device 110 to create a copy or an instance of the learnermicroservice on the user device 106 and/or supervisor device 110.

After the microservice has been launched, the package data assetcorresponding content request received in block 904 is retrieved. Insome embodiments, this package-data asset can be retrieved by thelearning asset retriever of the custom user microservice from thelearning asset store of the learning asset provisioning microservice1035. After the package-data asset has been retrieved, the process 900proceeds block 924 wherein enriching metadata is extracted from thepackage-data asset. This enriching metadata can specify attributes ofthe content associated with package-data assets such as, for example,the type of the content associated with package-data assets and/orcapability requirements for providing the content associated with thepackage-data asset.

After the enriching metadata has been extracted, the process 900proceeds to block 926 wherein a rendering of the package-data asset isdetermined. In some embodiments, this can include the determination ofthe viewers required to launch and/or present the content. After therendering has been determined, the process 900 proceeds to block 928wherein the package-data asset and/or the content associated with thepackage-data asset is rendered by the learner microservice, and theprocess then proceeds to block 929, wherein the content requested inblock 904 is delivered to the user via the learner microservice. In someembodiments, the delivery of the content can comprise the delivery ofone or several package-data assets, each of some or all of thepackage-data assets can include a link to associated content. In someembodiments, and upon receipt of the one or several package data assets,the content associated with the package data assets can be automaticallyretrieved via the links associated with each of the some or all of thepackage-data assets.

With reference now to FIG. 27, a flowchart illustrating one embodimentof a process 2350 for content delivery via an individualized and securedcontent delivery microservice. The process 2350 can be performed by allor portions of the global architecture 810 including, for example, theengagement delivery module 1020 and/or the user microservice 2250. Insome embodiments, for example, the process 2350 can be performed by theglobal architecture 810 when a platform user microservice 2250-A isused, and the process 2350 can be performed by the user device 106, whena local user microservice 2250-B is used.

The process 2350 begins at block 2352, wherein user information isreceived. In some embodiments, this can comprise user login informationand/or information identifying content and/or a group of content, suchas a course requested by the user. The user information can be receivedAPI management and security module 1015, and specifically by the APIgateway 970. This information can then be sent to, for example, thecross-cutting services 1055, and specifically to, for example, theinter-process communication service 2730, and/or to the serviceorchestrator 1065 that can, for example, communicate with the DAM 2805to determine the validity of the received user information. In someembodiments, alternatively, the user information can be received by theuser device 106, and specifically can be received by the clientapplication running on the user device 106, which client application caninclude the user microservice 2250. In such an embodiment, the receiveduser information can be validated by the authentication andauthorization module 2405.

After the user information is received, the process 2350 proceeds toblock 2354 wherein sandbox is launched. The sandbox can be launchedwithin the client application of the user device 106 and/or can belaunched within the engagement delivery module 1020. The sandbox cancreate a controlled environment, wherein user microservice 2250 canoperate so as to mitigate vulnerability of the global architecture 810.In some embodiments, the sandbox is unique to the user, is unique to theuser microservice, or the like. In such an embodiment, the sandbox to belaunched is identified based on user information received in block 2352,and the identified sandbox is then launched.

After the sandbox is and launched, user microservice 2250 is launched inthe sandbox as indicated in block 2356. In some embodiments, the launchof the user microservice can include the determination of whether anappropriate user microservice 2250 already exists. If such a usermicroservice 2250 does not exist, then the appropriate user microservice2250 can be generated according to the process of FIG. 26.Alternatively, if it is determined that the appropriate usermicroservice 2250 exists, then this user microservice 2250 can beidentified and can then be launched. In some embodiments, theappropriate microservice can be a microservice customized for the userand/or customize for the user and the requested content or group ofcontent.

In embodiments in which the process 2350 is performed by the user device106, user microservice 2250 can be launched by the client app of theuser device 106, whereas in embodiments in which the process 2350 isperformed by the global architecture 810, the user microservice can belaunched by the engagement delivery module 1020, and specifically by themicroservice builder 2235 of the engagement delivery module 1020.

After the launch of the user microservice, the process 2350 proceedsblock 2358 wherein a content request is received. In some embodiments,the content request can be received by the API 2430 of the usermicroservice 2250. After the content request is been received, theprocess 2350 proceeds to block 2360 wherein one or several internalcontent selection features are triggered. In some embodiments, thesefeatures can include, for example, the learning asset sequencer 2410,and/or the recommendation engine 2415. In some embodiments, thesefeatures can all be located within the user microservice 2250, in someembodiments, the user microservice can query other components of theglobal architecture 810 for requested content via, for example, theplatform synchronizer 2440, which can communicate with the Gateway API970. Thus, for example, in embodiments in which the process 2350 isperformed by the global architecture, the internal content selectionfeatures can include, for example, the course data manager 2210, thealerting data manager 2215, the experience composer module 1025, productbuilder module 1030, the asset provisioning module 1035, and/or themodel builder module and 40.

After the triggering internal content selection features, the process2350 proceeds to block 2362. Where next content is identified. In someembodiments, wherein next content is identified. In some embodiments,next content can be identified according to the knowledge graph, whichcan be generated, for example, in the process depicted in FIG. 23. Thisnext content can, in some embodiments, be adaptively selected and/or benon-adaptively selected. The next content can be selected by thelearning asset sequencer 2410 and/or by the recommendation engine 2415.

After the next content has been identified, the process 2350 proceeds todecision state 2364, wherein it is determined if the next content isinternally stored, and specifically if the next content is stored in thedatabase 2435 of the user microservice 2250. This can include queryingthe database 2435 for the next content and receiving a response from thedatabase 2435 indicating the presence or absence of the next content inthe database 2435.

If it is determined that the next content is internally stored, then theprocess 2350 proceeds to block 2366, wherein the next content isretrieved and/or received. In some embodiments, the next content can bereceived and/or be retrieved by the learning asset retriever 2420.

Returning again to decision state 2364, if it is determined that thenext content is not internally stored, then the process 2350 proceeds toblock 2370 wherein a content query is generated. In some embodiments,the content query can be generated by the learning asset retriever 2420,which learning asset retriever 2420 can fetch one or severalpackage-data assets from the database 1425 of the learning assetprovisioning microservice 1035. In some embodiments, the content querycan comprise a request for providing of one or several package-dataassets. This request can comprise, for example, one or severalelectrical communications. After the content query has been generated,the process 2350 proceeds to block 2372, wherein the content querytraverses the sandbox. In some embodiments, this can include anevaluation, the content query to determine the safety of the contentquery and/or to identify any unallowable of the content queries orcontent contained within the content queries. After the evaluation ofthe content query, the content query can be provided from the usermicroservice 2250 to, for example, the cross cutting services 1055, andspecifically to the inter-process communication service 2730. In someembodiments, the content query can comprise an event, incorporated intoa data stream generated by the cross cutting microservices 1055 in theirfunction as a virtual bus.

After the traversing of the sandbox, the process 2350 proceeds to block2374, wherein next content is received. In some embodiments, this nextcontent can be received by the learning asset retriever 2420. After thenext content has been received, or after the next content has beenretrieved, as indicated in block 2366, the process 2350 proceeds toblock 2368 wherein the next content is provided. In some embodiments,the next content can be provided to the user of the user device 106 via,for example, the I/O subsystem 526 of the user device 106.

After the next content has been provided, the process 2350 proceeds toblock 2376 wherein user data, and specifically user data internal to theuser microservice 2250 is updated. In some embodiments, this can includeupdating the database 2435 to reflect a change state of the user, andspecifically to reflect the providing of the next content. After theuser data has been updated, the process 2350 proceeds to block 2378,wherein a message identifying the updated user data, and specificallythe change state of the user is sent to the global architecture 810. Insome embodiments, this message can be generated and sent by the platformsynchronizer 2440 in communication with, for example, the Gateway API970. In addition to this, and in some embodiments, simultaneous with thecommunicating with the Gateway API then hundred 70, activity data can besent to the activity stream API 978.

With reference now to FIG. 28, a flowchart illustrating one embodimentof a process 2600 for off-line operation of the user microservice 2250is shown. The process 2600 can be performed by the user microservice2250, and specifically by an instance of the user microservice locatedon the user device 106 operating unconnected with the globalarchitecture.

The process 2600 begins at block 2602, wherein user information isreceived. In some embodiments, this can comprise user login informationand/or information identifying content and/or a group of content, suchas a course requested by the user. The user information can be receivedby the user device 106, and specifically can be received by the clientapplication running on the user device 106, which client application caninclude the user microservice 2250. In such an embodiment, the receiveduser information can be validated by the authentication andauthorization module 2405.

After the user information has been received, the process 2600 proceedsto block 2604, wherein a local user microservice 2250-B is requested. Insome embodiments, this request can be made by the user device 106 to theglobal architecture 810, and specifically to the gateway API 970 whichcan communicate the request, in some embodiments via the cross-cuttingservices 1055, to the engagement delivery module 1020 and specificallyto the microservices builder 2235 of the engagement delivery module1020.

After the request for a local instance of the user microservice 2250-B,the process 2600 proceeds to block 2606, wherein the local instance ofthe user microservice 2250-B is received. In some embodiments, this canbe received by the user device via the communication network, andspecifically via communications with, for example, the gateway API 970.In some embodiments, the receipt of the local instance of the usermicroservice 2250-B can be accompanied by receipt of informationrelating to the user state, and specifically to the user's location inthe knowledge graph. In some embodiments, this information can beretrieved by the learning asset retriever as part of the receipt of thelocal instance of the user microservice 2250. In some embodiments, oneor several potential next learning assets can likewise be received aspart of the receipt of the local instance of the user microservice 2250.These can be accompanied by information for use by the scoring engine2425 in evaluating responses received one or several learning assets.Similarly, sequencing information for provided learning assets can belikewise provided to the local instance of the user microservice 2250.This information can include one or several models for adaptivesequencing, one or several rules or prerequisite relationships fornon-adaptive sequencing, or the like. Information received simultaneouswith the receipt of the local user microservice 2250-B can be stored inthe database 2435.

After the local user microservice 2250-B has been received, the process2600 proceeds to block 2608, wherein the connection with the globalarchitecture 810 is lost. While depicted between blocks 2606 and 2608,the connection with the global architecture 810 can be lost at any pointin the process 2600.

At block 2610, the local instance of the user microservice 2250, andspecifically the local user microservice 2250-B is launched. In someembodiments, the local instance of user microservice 2250 can belaunched within the client application of the user device 106. After thelaunch of the local instance of the user microservice 2250, the process2600 proceeds block 2612 wherein a content request is received. Thecontent request can be received by the I/O subsystem 526 of the userdevice and can be provided to the user microservice 2250. In someembodiments, the content request can be directed to the learning assetsequencer 2410, and/or, the recommendation engine 2415.

After the content request is been received, the process 2600 proceeds toblock 2614 wherein content is selected and provided. The content can beselected and provided by one or both of the learning asset sequencer2410 and the recommendation engine 2415. In some embodiments, thecontent, which can comprise one or several package-data assets can beselected by identifying the user's location in the knowledge graph, andidentifying the next package-data asset based on the user's location inthe knowledge graph, as well as, in some embodiments, one or severalattributes of the user and/or the package-data asset. After the nextpackage-data asset has been identified, the learning asset retriever2420 can identify the next package-data asset within the database 2435,and can retrieve the next package-data asset.

After the next package-data asset has been selected, and/or provided,the process 2600 proceeds to block 2616, wherein a response is received.The response can be received by the I/O subsystem 526 of the user device106 and can be provided to the scoring engine 2425. At block 2618 ofprocess 2600, the received response is evaluated. In some embodiments,the received response can be evaluated by the scoring engine 2425,according to data stored in the database 2435. In some embodiments, thisdata can specify one or several rules for evaluating the receivedresponse, and/or criteria and/or attributes of a correct response. Insome embodiments, the scoring engine 2425, can determine whether thereceived response is correct or incorrect, and/or determine the degreeto which the received response is a correct response. In someembodiments, this can include determining a percent correct and/orpoints earned by the received response.

After the responses been evaluated, the process 2600 proceeds to block2620 wherein one or several models are updated. In some embodiments, forexample, this can include updating a model characterizing one or severalattributes of the user such as, for example, a user skill level, a usermastery information, or the like. After the model so been updated, theprocess proceeds to decision state 2630, wherein it is determined if anadditional content request is received. If an additional content requestis received, then the process returns to block 2614, and proceeds asoutlined above. Alternatively, if an additional content request is notbeen received, then the process 2600 proceeds to block 2632, and awaitsa content request.

Simultaneous with, or after the completion of any of steps 2614 through2620, the process 2600 can simultaneously proceed to block 2622 whereinstate data is updated. In some embodiments, this can include, forexample, updating state data within the user microservice 2250 toreflect provided content, a received response, and evaluated response,and/or the result of the evaluated response, and/or one or severalupdated models. The state data can be updated and can be stored in thedatabase 2435.

After the state data has been updated, and/or simultaneous with theupdate of the state data, the process proceeds to decision state 2624wherein it is determined if a connection has been reestablished. If theconnection has not been reestablished, then the process 2600 proceeds toblock 2628 and awaits for the reestablishment of a connection with theglobal architecture 810. Alternatively, if connection has beenreestablished, or after connection has been reestablished, the process2600 proceeds to block 2629, wherein state data on the local usermicroservice 2250-B is synchronized with state data in the globalarchitecture. In some embodiments, the synchronization can be performedby the platform synchronizer 2440. Additionally, upon reestablishment ofthe connection between the local instance of the user microservice 2250and the global architecture 810, event data that was captured while thelocal instance of the user microservice 2250 was unconnected can beprovided to the event stream API 978.

The Analytics Module

With reference now to FIG. 29, a schematic illustration of oneembodiment of the analytics components 1060, also referred to herein asthe analytics module 1060 or the analytics subsystem, is shown. Theanalytics component 1060 can provide insight into the historical datacaptured from the recipient-user activity stream, the globalarchitecture 810 APIs, and potentially other sources as well, in orderto produce continuously updated models for reporting, royalties, usage,as well as to drive the learning experience. These models can assessmastery of learning objectives, efficacy of package-data assets, predicta recipient-user's behaviour or provide the data needed to recommend anadaptive path to follow during the learning of course. The analyticscomponent 1060 also accumulates the information on which content hasbeen viewed, and provides the data needed for reporting on utilizationand other business and institutional management data. The analyticcomponent 1060 supports both “batch” as well as near real-time eventprocessing.

The analytics subsystem 1060 provides facilities to collect data at highspeed, execute analytic models against that data, and to persist andsupply access to the results. The analytics subsystem 1060 can providethe facilities of an “Analytics as a Service” (“AaaS”) system to therest of the global architecture 810 via plug-in model files, and permitsaccess to analytic results to other subsystems at high speed through itsAPI 2930. The analytics subsystem 1060 can also support near-real time(tens or hundreds of millisecond) notifications of events through acomplex event processing subsystem and a pub/sub notification model.

In some embodiments, for example, the analytics subsystem 1060 receivesthe activity stream from the user device 106 and/or the client as wellas other events passing through the global architecture 810. Theanalytics subsystem 1060 executes models against these and, along withother responsibilities, maintains constantly updated: student modelsthat identify one or several student's mastery of learning objectives;efficacy models identifying the efficacy of one or several learningassets 1420; one or several difficulty models characterizing thedifficulty of one or several package-data assets; one or severalpredictive and diagnostic models showing systematic sources of errorthat can be used as inputs into dynamic adaptive learning andinformation on which content has been viewed that is needed to calculateroyalties.

The analytic subsystem 1060 can monitor student activity and triggerappropriate actions in near real-time. For example, the system may raisea “Do you need help?” alert to a given student when his or her actionsindicate that they are having trouble with a particular concept. Theanalytic subsystem 1060 can provide a place where machine-learning andother services can be plugged in. For example, machine-learningalgorithms can be used to identify systematic sources of error forparticular assessment questions, and return not only learning masteringinformation but also coaching tips that can be used by therecommendation engine to address specific misconceptions. The analyticsubsystem 1060 can comprise the following components: the API 2930; thedata ingestion interface 2905; the persistence manager 2910; the datacollector 2915; the filters and enrichers 2920, the aggregators 2935;the modelers and analyzers 2940, the batch view generator 2955; thereal-time generator 2960, the complex events processor 2945; and datastore 2925.

The API 2930 can allow other global architecture 810 components toobtain information from the analytics subsystem 1060. The API 2930 canbe used to call other global architecture 810 components. The dataingestion interface 2905 accepts incoming activity stream messages andAPI traffic at high speed and classifies it as either batch-mode data,that can be processed on a scheduled basis, or as data that must beprocessed in near real-time, such as alerts or requests for help. Thedata ingestion interface 2905 is a logical construct, and defines asingle interface for each datatype, formatting the data for that typeappropriately before ingesting it into the global architecture 810. Thedata ingestion interface 2905 may also be used to load batch data intothe global architecture 810.

The persistence manager 2910 can manage the storage of incoming,unprocessed data for logging, journaling and later processing and datamining. When the data is used by the analytics subsystem 1060 for batchand near real-time view generation, the data is stored in a polyglotpersistent store, with the particular storage mechanism chosen based onthe type of data. The persistence manager 2910 can be a logical unitwhich, in some cases, can be physically part of the client SDK. Thepersistence manager 2910 logical function can identify the data sourceand format of incoming data, and decide on the appropriate place andformat in which to store it persistently. The persistence manager 2910also profiles the data before storing it, in order to ensure that thedata meets the particular security concerns of that data set. In someembodiments, for example, the persistence manage 2910 can identify aexclude PII data from being persisted.

The data collector 2915 caches and groups messages together. In someembodiments, this can occur in instances where meaning is spread acrossmultiple messages. The data collector 2915 also untangles messagestreams so that messages relevant to a given client app or sequence ofactivity are serialized. In some embodiments of the analytics subsystem1060 in the global architecture 810 there are two layers, a “batch”layer and a “speed” layer. The “the batch view generator 2955 generatesthe batch layer and computes those analytic results that are toocomputationally intensive to be done in near real-time. Typically, theseoperations will take minutes to hours to run, though they may take aslittle as tens of seconds. Whatever is “too long” for the user to waitfor is done in “batch” mode. These batch operations can include, forexample, big-data operations such as large map-reduce jobs that take arelatively long time to run. These long-running analytic operations canbe performed on a scheduled or semi-continuous basis, hence the name“batch” layer.

The function of the batch layer can be both to (a) compute and maintainmodels that are relatively computationally intensive (for example, thestudent model) based on the latest data available, and (b) supply datathat is combined with that of the speed layer so that decisions are madein near “real-time” using the “batch” data as context. For example, bycombining speed layer and batch-layer data, a recommendation can begiven in a couple of hundred milliseconds that takes the most up-to-datestudent model (tens of seconds or minutes old) into account. The outputof the batch layer is both persisted and surfaced in a view where it canbe combined with that generated by the “speed” layer to make contextaware, near real-time decisions. The real-time view generator 2960 isthe counterpart of the batch view generator 2955 discussed above, andcorresponds to the “speed” layer global architecture 810 architecture.The speed layer executes analytic models that describe what is happeningright now, in approximately the last 100 ms. As used herein,approximately identifies a range extending +/−5% around the therewithassociated value, +/−10% around the therewith associated value, +/−15around the therewith associated value, +/−20 around the therewithassociated value, and/or any other or intermediate range around thetherewith associated value. The speed layer is typically implementedusing streaming technology. The results of speed layer processing aresurfaced in a “real-time view” (more properly “near real-time view, asthis data is typically a few hundred milliseconds old). The modelprocessing components described below combine the “what's happening now”information supplied by the real-time view generator 2960, with the“context” information supplied by the batch view generator, to makecontextualized near-real time decisions.

Filters and enrichers 2920 are two types of data transformationoperations that the system can perform on combined batch and “real-time”view data. A filter screens out data unless it conforms to a certainpattern. An enricher adds data to what is already there, providedcertain criteria are met. The application of these operations isdetermined by a model file. An aggregator 2935 combines data in certainways defined by a model file. The aggregator 2935 gathers, groups,consolidates and summarizes information. Modelers and analyzers 2940implement complex mathematical and other analytical models against data.They are driven by a model file that specifies which operations toperform. The complex event processor 2945 looks at the stream of alreadyanalyzed information and decides based on rules or analytic models whichactions to take. The system is “complex” because it can evaluatemultiple messages and multiple pieces of data and combine them in waysthat are much more sophisticated than simple event-based rules. Themessage publisher 2950 is used by the complex event processor 2945 tonotify interested parties that a complex event has occurred—for example,a student has asked for help, and that student is (a) currently enrolledin the course; (b) struggling with concepts x, y & z; (c) has beenassigned a peer tutor, and (d) that peer tutor is out of town onholiday. Data stores 2925 can be used to (a) persist raw, incoming datafor logging, journaling and data mining purposes; (b) store processedanalytic results, such as the student model, efficacy model, difficultymodel, etc.; (c) provide federated or proxy access (views) to externaldata stores, such as student information systems (SIS) and learningmanagement systems (LMS), whose contents are used within analyticmodels.

With reference now to FIG. 30, a flowchart illustrating one embodimentof a process for hybrid event processing is shown. The process can beperformed by the global architecture 810, and specifically by theanalytic subsystem 1060. The process begins a block 2652 wherein anevent stream is received. In some embodiments, the event stream isgenerated by the event stream API 978 based on activity data receivedfrom one or several client applications running on one or several userdevices 106. The event stream can be received by the data ingestioninterface 2905.

After the event stream has been received, the process 2650 proceeds toblock 2654 wherein events for immediate processing identified. In someembodiments, for example, the data ingestion interface 2905 can acceptincoming activity stream messages and API traffic at high speed and canclassify events as either batch-mode data, that can be processed on ascheduled basis, or as data for processing in near real-time, such asalerts or requests for help. In some embodiments, for example, the dataingestion interface 2905 can classify events as either for immediate orbatch processing based on one or several attributes of the events, whichattributes can be carried and metadata associated with those events.

After events have been identified for media processing, the process 2650proceeds to block 2656, wherein linked messages and/or events areidentified. In some embodiments, for example, a message in the eventstream may only have meaning in the context of one or several othermessages. In some embodiments, identifying linked messages and/or eventscan include the operation of the data collector 2915 to identify suchmessages and/or events, and specifically to cache and group messagestogether when meaning or an event is spread across. In some embodiments,this can include receiving event data, determining if meaning is spreadacross multiple messages and/or events, and when meaning is spreadacross multiple messages and/or events, caching and/or storing therelevant messages and/or events. In some embodiments, the data collector2915 can further untangle and/or serialize these cached and/or storedmessages and can properly sequence these cached and/or stored messagesto facilitate evaluation of these messages and/or events and theextraction of meaning from the same.

After the linked messages and/or events have been identified, theprocess 2650 proceeds to block 2658, wherein events for identified forimmediate processing are sent for immediate processing. In someembodiments, this can include the sending of these messages and/orevents to the real-time view generator 2960. In some embodiments, suchmessages and/or events can be filtered and/or enriched by the filtersand enrichers 2920, and/or aggregated by the aggregators 2935. In someembodiments, these messages, and/or events can be used by the modelersand analyzers 2942 update one or several models.

At block 2660 events and/or messages for batch processing identified. Insome embodiments, this can be performed by the data ingestion interface2905 and can be performed simultaneous with the step of block 2654. Insome embodiments, messages, and/or events identified for batchprocessing can be managed by the persistence manager 2910, which canstore these messages, and/or events, and which can generate batches forbatch processing as indicated in block 2662. In some embodiments, thesebatches can be defined by the passing of a predetermined amount of timesince the creation and/or generation of the last batch.

When a batch has been completely formed, such as when the amount of timefor aggregation of a batch has passed, the batch can be sent forprocessing. In some embodiments, the batch can be sent by thepersistence manager 2910 to the batch/view generator 2955. Forprocessing. The batch/view generator 2955, can filter and/or in richevents within the batch via the filters and enrichers 2920, and/oraggregate the events within the batch via the aggregators 2935. In someembodiments, batch processing can be used to update one or severalmodels with the modelers and analyzers 2940.

In some embodiments, subsequent to the performing the batch processing,the batch/view generator 2955 outputs a data stream to the CFP 2945.Similarly, subsequent to the performing of immediate processing, thereal-time view generator 2960 outputs a data stream to the CFP 2945. TheCFP 2945, can evaluate the stream of data, and, based on rules oranalytic models, identify one or several actions. In some embodiments,these one or several actions can address an anomaly identified by theCEP 2945. Subsequent to processing by the CFP 2945, the messagepublisher 2950 can publish the results of one or both of the batchprocessing, and the immediate processing, as well as the results of theprocessing by the CFP 2945.

At block 2666, a content request is received, the global architecture810. At block 2668 this received content request is parsed into a speedcomponent for which data from speed processing is desired, and a batchcomponent for which data from batch processing is desired. After theparsing of the request, the process 2650 proceeds to block 2670 whereininformation from the speed layer or from immediate processing isreceived. In some embodiments, this is received via the digest outputtedby the message publisher 2950. At block 2672, a snapshot of one orseveral on updated batch component information is received. In someembodiments, this snapshot may not reflect variation due to eventsdesignated for batch processing that have not yet been processed.However, batch component information may not be sensitive to suchprocessing delays.

After the snapshot of un-updated batch components has been received, theprocess 2650 proceeds to block 2674 wherein a response is generatedbased on a combination of speed and batch components. Once thisresponses been generated, the process 2650 proceeds to block 2676wherein a response to the content request of 2666 is provided.

Cross Cutting Services

With reference now to FIG. 31, a schematic illustration of the crosscutting services microservice 1055, also referred to herein as thecommunications microservice 1055 is shown. The communicationsmicroservice 1055 can provide the services that have a scope across allother microservices in the global architecture 810. These services caninclude, for example, either the infrastructure services such aslogging, monitoring, auditing, inter-process communication, etc. or theconsumer services such as notification services, reports, etc. The APImanagement and security microservice 1015 can be the entry point intothe global architecture 810. The services published by the globalarchitecture 810 can be accessed through the API management and securitymicroservice 1015, which in turn can invoke the appropriatemicroservices of the global architecture 810. The API management andsecurity microservice 1015 can be the gatekeeper of the globalarchitecture 810 and manages, authenticates, discovers, and routesrequests from global architecture 810 API consumers. In someembodiments, the communications microservice 1055 can function as avirtual communication bus that can receive data inputs from othercomponents and/or microservices of the global architecture 810 and canuse those received data inputs to generate a digest which is thenoutputted by the communications microservice 1055. This digest can becontinuously delivered in real-time as new data is received by thecommunications microservice 1055. The other components and/ormicroservices of the global architecture 810 can subscribe to thisdigest and can filter and/or scan the digest for information. When thisinformation is identified, this information can be used by the othercomponents and/or microservices to perform one or several actions.

The cross-cutting services microservice 1055 “cut across” all of theother microservices on the global architecture 810. Cross-cuttingservices microservice 1055 comprises multiple microservice including thereports microservice 2705, the communication services/tools microservice2710, the search microservice 2715, the software configurationmanagement microservice 2720, the monitoring, auditing and alertingmicroservice 2725, inter-process communication service microservice2730, notification service microservice 2735, app/widget storemicroservice 2740, and platform synchronization services microservice2745. The reports microservice 2705 allows clients access, via datamarts, to the data warehouse that stores the reports as well as otherdata retrieved from the global architecture 810. The reportsmicroservice 2705 can be an external engine which supports the followingdescribed functions. Analytical query that encapsulates the informationresulting from running any standard statistical, predictive or machinelearning algorithms. SQL query function that can be a standard SQL92/2011 compliant query of one of following three types: BQ—signifyingthe type which is required to run only on batch mode; RQ—signifying thequery which needs the access of most recently produced data; andCQ—signifying the query which needs to run against both ancient andrecently produced data. The reports microservice 2705 can createpre-processed reports. This request executes and produces apre-processed report that was previously created and stored for laterretrieval. The reports microservice 2705 can asynchronously consolidatethe data from all of the microservices in the global architecture 810and/or in the cross-cutting services microservice 1055. In someembodiments, this can free the rest of the global architecture 810 fromperformance and scaling issues. To support near real-time reporting, theentire paradigm can support eventual consistency using steaming servicessuch as AWS Kinesis/Spark Streaming to keep updating the reportingdatabase, so that the reporting is as close to real-time as is possible.

The search microservice 2715 can be a service which contains all theindexed data from the other microservices databases and acts as a freetext search microservice. As for the reports, the same principle ofcreating a microservice to receive the data asynchronously as well asvia streams is enabled. The query service provides full text searches.The search microservice 2715 automatically re-indexes data through ascheduler service, which re-indexes the data based on rules. The searchmicroservice 2715 supports the following options: fielded search;free-text search; pagination; faceting or delivering searches based onaspects and facets; and caching of popular searches. Because of theadaptive display of resources to recipient-users, personalization ofsearch results to an individual recipient-user may take into account thepaths the recipient-user has taken and/or may potentially take or havetaken in traversing the available content. In other words, in anadaptive situation the recipient-user will not necessarily see all thecontent that is potentially available to them within a given product, sothe search results are tailored according to the paths already taken orpotentially to be taken.

The software configuration management microservice 2720 provides an APIto track and control changes in the global architecture 810 and softwarebeing used in the global architecture 810. This includes revisioncontrol and the establishment of baselines. Since the globalarchitecture 810 is an auto scaling, self-configuring self-managingentity, this service controls and tracks changes made to the service inorder to help with the following: providing accurate configurationinformation to assist decision making, e.g. the authorization ofchanges, the planning of releases, and to help resolve incidents andproblems faster; and maintaining accurate configuration by ensuring thedefinition of control of the components making up a service and itsinfrastructure.

The monitoring, auditing and alerting microservice 2725 comprisesmonitoring, auditing, and alerting components. The monitoring componentprovides real-time insight into how many instances are deployed, depthof queues, resource utilization, and other factors. The auditingcomponent can identify any data that has been changed in the systemand/or within any entity. Auditing component functions can include: theauditing component captures the data that has been changed hat is thensent asynchronously to the appropriate API; the auditing componentcaptures all data changes in a sequential manner, with timestamps; allentities in the system are audited, and all changes are pushed viaasynchronous methods to a data store; the auditing component can bedurable, highly reliable and fault tolerant to insure changes arecaptured; and the auditing component can provide an easy way to view theAPI/engine to show the audit trail for any entity in the system, andallow mapping to the corresponding audit trail. The alert component canraise alerts, which alerts can identify one or several problemsidentified, in for example, the audit trail for any entity in thesystem.

The inter process communication service microservice 2730 provides aconsistent way for different microservices to queue events or actionsone after the other, so that a larger workflow encompassing multiplemicroservices is enabled. The inter process communication servicemicroservice 2730 provides a stable and consistent way for allmicroservices to communicate with each other and any microservice can becalled or can call any other microservice provided that certainconditions and policies are met. In short, the inter processcommunication service microservice 2730 can implement a message-passinginterface which allows for request-reply as well as pub-sub models toprovide for use of services where services “know” each other as well aswhen services do not have knowledge of each other. The notificationservices microservice 2735 provides a mechanism to message othersystems/devices/users or clients when a near-real time alert conditionoccurs or when the system needs to send a communication apprising theentity of the current situation. When other microservices need to raisea notification, they call the notification services microservice 2735service which does the actual job of raising the notification.

The APP/WIDGET STORE microservice 2740 comprises a plurality of widgets.Widgets can be UI components developed to render a particular service orgroup of services provided by the global architecture 810. UI componentscan have the ability to operate in two modes, offline and online, andare downloaded into the client app as “plug-and-play” based on need andcurrent task. Players can be a specific type of components which renderspecific types of learning content provided by the global architecture810, generally for reverse compatibility with legacy content types. Theuse of “Origami” widgets to populate components and to render textmetadata (such as section headings) gives the global architecture 810the option of ensuring the same look and feel across devices—forhigh-stakes assessment preparation, for example—or using thedevice-native experience that many users prefer. Widgets/Componentsprovide global architecture 810 resources (for example, a note takingwidget), interactivity (e.g. choice lists for assessment questions), andalso render content, including narrative, “rich” and assessment-typecontent. The global architecture 810 does not itself contain content;rather it contains references to the required content, and to metadataassociated with that content. These references are created when contentis provisioned. When content or associated content metadata is renderedby the client, the client fetches it directly from the CMS or CDNwithout going through the global architecture 810. The allows formaximum scale and for the use of content delivery networks to cachecontent at the edges of the network to minimize retrieval times.

Enabling Services

With reference now to FIG. 32, depicts a detailed block diagram of theenabling services component 1070 in connection to the serviceorchestrator component 1065. Service orchestration can be the process ofintegrating two or more services together to automate a process, orsynchronize data in real-time. The global architecture 810 uses aService Orchestrator 1065 in this architecture to provide the globalarchitecture 810 with a single, simple and consistent methodology tointeract with all 3^(rd) Party Data Sources. The service orchestratorcomponent 1065 communicates with the enabling services component 1070over an enterprise service bus. The enabling services component 1070 iscomprised of components including: an identity access and managementcomponent 2805, a learning management solution component 2810, ane-commerce component 2815, a content platform component 2820, a billingcomponent 2835, and any other components 2830.

The identity access and management component 2805 provides singlesign-on (SSO), social sign-on, adaptive authentication, strongauthentication, federation, self-service, adaptive risk, web servicessecurity, and fine-grained authorization. At the highest level identityaccess and management component 2805 consists of a single,self-contained Java application; service components such as stateful orstateless session management; client-side APIs in C, Java, and REST;service provider interfaces to enable custom plugins; and policy agentsfor web and app server containers to enforce access policies toprotected web sites and services. The learning management solutioncomponent 2810 is a software application for the administration,documentation, tracking, reporting and delivery of electroniceducational technology (also called e-learning) courses or trainingprograms. Colleges, universities and schools use the learning managementsolution component 2810 to manage their students and the globalarchitecture 810 integrates with them, so as to provide a seamlessexperience to the recipient-user. The learning management solutioncomponent 2810 is a framework that handles all aspects of the learningprocess. It delivers and manages instructional content, identifies andassesses individual and organizational learning or training goals,tracks the progress towards meeting those goals, and collects andpresents data for supervising the learning process of the organizationas a whole.

The global architecture 810 supports the ability of recipient-users tobuy titles and to show these products based on a pricing strategy thatneeds to support globalization, payments, etc. using the e-commercecomponent 2815. The e-commerce component provides web catalogmanagement, order management, a shopping cart, and a payments engine.Web catalog management: is a system that ensures the quality of the dataand its configurability to the recipient-user's required format. It's asystem that allows suppliers to quickly broadcast updates andintroduction of new items. Order Management is the administration ofbusiness processes related to orders for global architecture 810products. The system automates and streamlines order processing forbusiness. The system generally manages vendors and order fulfilment. Theshopping cart is a digital place where recipient-users can store globalarchitecture 810 products and construct an order for eventualconsumption through multiple channels, and the ecommerce system canmanage and maintain the cart for each and every user of the system. Thepayments engine: can be a streamlined end to end payment processing onthe global architecture 810.

FIG. 33 is a detailed block diagram of applications 1005. At a highlevel, Application 1005 functions are distributed into the componentsdescribed below. The Software Development Kit is a set of libraries thatinteract with the global architecture 810 APIs and provide variousfacilities—such as default widgets, dynamic widget loading mechanisms,etc.—that are useful to application developers on a given globalarchitecture 810. There can be multiple SDKs, each specific to theirrespective Web or device OS (iOS, Android, etc.) global architecture810. In some embodiments, player can be the end user applications orclient which render the learning content provided by the Global learningplatform 810. From the perspective of the global architecture 810,Applications 1005 can be the UI for the various microservices that makeup the Global learning platform 810. As used herein, the combination ofan SDK, along with a container, with components and Players make up anapplication.

FIG. 34 is detailed block diagram of one embodiment of a clientarchitecture 3400 of a client app. In some embodiments, a single clientarchitecture 3400 can be provided and/or customized for each environmentsupported—one client architecture 3400 for the Web, one clientarchitecture 3400 for iOS, etc. The “look and feel” of the client UI canbe configurable through the content plan, style sheets, and therendering components that are dynamically loaded. In other words, theclient can be an all-purpose “reader” for all types of native content ofthe global architecture 810.

The client architecture 3400 can be built around a “software developmentkit” (SDK) 3402 that can control and/or regulate interactions with theAPI gateway 970. The SDK 3402 can be a set of libraries. The SDK 3402can be provided for the Web, and additional SDKs 3402 can be specific toeach OS or platform to be supported. In some embodiments, each SDKencapsulates the same functionality for the implementation of clients)on each platform (Web, Android, iOS, Xbox, etc.). In some embodiments,each of the clients supporting the client architecture 3400 will havethe SDK 3402 to “talk” to the global architecture 810. In someembodiments, for example, the client architecture 3400 can comprise afirst SDK 3402-A that can facilitate communication with the API gateway970 and a second SDK 3402-B that can facilitate communication with theactivity stream API 978.

The client architecture 3400 can include a plurality of components 3404.The components 3404 can be widgets or UI components developed to rendera particular service or group of services provided by the Platform. Thecomponents 3404 can provide resources (for example, a note takingwidget), interactivity (e.g. choice lists for assessment questions), andalso render content, including narrative, “rich”, and assessment-typecontent. The client architecture 3400 can further include a playercontainer 3406. The player container can facilitate in the properloading and/or unloading of components or other resource components.

Additionally, in some embodiments, the client architecture 3400 caninclude a user microservice 2250 and an event collector 3408. The eventcollector can generate a semanticized stream of collected activity datacharacterizing user interactions with all or portions of the clientarchitecture 3400. In some embodiments, this semanticized stream ofcollected activity data can be formulated into one or several events. Insome embodiments, the event collector 3408 semanticizes and sends to theglobal architecture 810 each interaction had by the user with the userdevice 106 operating the client app. In some embodiments, these eventsand/or the semanticization of the events can be performed by the eventcollector 3408, and in some embodiments, these events and/or thesemanticization of the events can be created by other portions of theclient app or client architecture 3400.

The event collector 3408 can send the events to the second SDK 3402-B,also referred to herein as the events SKD 3402-B. The events SDK 3402-B,sends the events received from the event collector 3408 to the activitystream API 978. In cases where the client app is disconnected and thelearner microservice 2250 is installed and running locally, the localinstance of the learner microservice 2250 can also receive these eventsfrom the events SDK 3202-B. The local instance of the user microservice2250 can use this event information to support current interactively,and also to cache the events locally for later transmission to theglobal architecture 810 when the client app re-connects with the globalarchitecture 810.

The components 3404 and/or the user microservice 2250 can connect to thecontainer 3406, which can in turn connect to the first SDK 3402-A. Thefirst SDK 3402-A can communicatingly connect with the API gateway 970 tosend data such as response data to the global architecture 810 and toreceive data such as one or several package-data assets from the globalarchitecture 810.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application-specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine-readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein, the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read-only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for hybrid content graph creation, thesystem comprising: a memory comprising: a learning-asset databasecomprising a plurality of learning assets each comprising a linkdirecting to educational content, wherein at least some of theeducational content is stored on a remote content platform; and acapability database including information identifying capabilities of aplurality of microservices, the plurality of microservices comprising: aproduct builder microservice; and a model building module; and at leastone server comprising the plurality of microservices, wherein the atleast one server is configured to: identify a set of learning assetsfrom the learning-asset database of the memory with the product buildermicroservice; retrieve with the product builder microservice a learningmodel associated with the set of learning assets from the model buildingmodule, the learning model comprising a plurality of sequencingguidelines; automatically determine a sequencing of learning assets inthe set of learning assets with the product builder microserviceaccording to the plurality of sequencing guidelines of the learningmodel, wherein the sequencing is partially adaptive-based and partiallynon-adaptive-based; and generate a graph-based representation of the setof learning assets based on the determined sequencing.
 2. The system ofclaim 1, wherein the learning model is retrieved from memory associatedwith the model building module.
 3. The system of claim 2, wherein the atleast one server is further configured to identify a first groupcomprising at least some of the set of learning assets as having a firstasset type and identify a second group comprising at least some of theset of learning assets as having a second asset type.
 4. The system ofclaim 3, wherein the at least some of the set of learning assetsincluded in the first group are different than the at least some of theset of learning assets included in the second group.
 5. The system ofclaim 4, wherein the first asset type comprises content-type learningassets and wherein the second asset type comprises non-content-typelearning assets.
 6. The system of claim 5, wherein the at least oneserver is configured to: (a) select a one of the learning assets in theset of learning assets; (b) determine a sequencing type for the selectedone of the learning assets, wherein the sequencing type identifies atleast one of: adaptive sequencing, non-adaptive sequencing, andpartially adaptive sequencing; and repeat (a) and (b) until a sequencingtype for all of learning assets in the set of learning assets isdetermined.
 7. The system of claim 6, wherein determining the sequencingcomprises, for non-adaptive learning assets: retrieving sequencinginformation; and sequencing the non-adaptive learning assets accordingto the retrieved sequencing information.
 8. The system of claim 7,wherein determining the sequencing comprises, for the adaptive learningassets: determining a skill level for learning assets having an adaptivesequencing type; and identifying prerequisite relationships between thelearning assets.
 9. The system of claim 8, wherein the at least oneserver is further configured to store non-content type learning assetsin the memory.
 10. The system of claim 9, wherein the at least oneserver is further configured to link content-type learning assets andnon-content-type learning assets.
 11. A method of automatic hybridcontent graph creation, the method comprising: identifying a set oflearning assets with a product builder microservice, each of thelearning assets comprising a link to educational content and associatedmetadata; retrieving with the product builder microservice a learningmodel associated with the set of learning assets from a model buildingmodule, the learning model comprising a plurality of sequencingguidelines; automatically determining with the product buildermicroservice a sequencing of learning assets in the set of learningassets according to the plurality of sequencing guidelines of thelearning model, wherein the sequencing is partially adaptive-based andpartially non-adaptive-based; and generating a graph-basedrepresentation of the set of learning assets based on the determinedsequencing.
 12. The method of claim 11, wherein the learning model isretrieved from memory associated with the model building module.
 13. Themethod of claim 12, further comprising identifying a first groupcomprising at least some of the set of learning assets as having a firstasset type and identifying a second group comprising at least some ofthe set of learning assets as having a second asset type.
 14. The methodof claim 13, wherein the at least some of the set of learning assetsincluded in the first group are different than the at least some of theset of learning assets included in the second group.
 15. The method ofclaim 14, wherein the first asset type comprises content-type learningassets and wherein the second asset type comprises non-content-typelearning assets.
 16. The method of claim 15, further comprising: (a)selecting a one of the learning assets in the set of learning assets;(b) determining a sequencing type for the selected one of the learningassets, wherein the sequencing type identifies at least one of: adaptivesequencing, non-adaptive sequencing, and partially adaptive sequencing;and repeating (a) and (b) until a sequencing type for all of learningassets in the set of learning assets is determined.
 17. The method ofclaim 16, wherein determining the sequencing comprises, for non-adaptivelearning assets: retrieving sequencing information; and sequencing thenon-adaptive learning assets according to the retrieved sequencinginformation.
 18. The method of claim 17, wherein determining thesequencing comprises, for the adaptive learning assets: determining askill level for learning assets having an adaptive sequencing type; andidentifying prerequisite relationships between the learning assets. 19.The method of claim 18, further comprising storing non-content typelearning assets in the memory.
 20. The method of claim 19, furthercomprising linking content-type learning assets and non-content-typelearning assets.